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Transcript
RETHINKING MACROECONOMICS: WHAT FAILED, AND
HOW TO REPAIR IT
Joseph E. Stiglitz
Article first published online: 28 JUN 2011
.
DOI: 10.1111/j.1542-4774.2011.01030.x
© 2011 by the European Economic Association
Issue
Journal of the European Economic Association
Volume 9, Issue 4, pages 591–645, August 2011
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Abstract
The standard macroeconomic models have failed, by all the most important
tests of scientific theory. They did not predict that the financial crisis would
happen; and when it did, they understated its effects. Monetary authorities
allowed bubbles to grow and focused on keeping inflation low, partly because
the standard models suggested that low inflation was necessary and almost
sufficient for efficiency and growth. After the crisis broke, policymakers relying
on the models floundered. Notwithstanding the diversity of macroeconomics,
the sum of these failures points to the need for a fundamental re-examination of
the models—and a reassertion of the lessons of modern general equilibrium
theory that were seemingly forgotten in the years leading up to the crisis. This
paper first describes the failures of the standard models in broad terms, and
then develops the economics of deep downturns, and shows that such
downturns are endogenous. Further, the paper argues that there have been
systemic changes to the structure of the economy that made the economy more
vulnerable to crisis, contrary to what the standard models argued. Finally, the
paper contrasts the policy implications of our framework with those of the
standard models.
Jump to…
1. Introduction
Those who claim to be disciples of Adam Smith should be unhappy with what
has happened in the last few years. The pursuit of self-interest (sometimes
called greed) on the part of bank executives did not lead, as if by an invisible
hand, to the well-being of all; in fact it was disastrous for the banks, workers,
taxpayers, homeowners, and the economy more broadly. Only the bankers
seemed to have fared well.1
Modern general equilibrium theory has explained why markets are almost never
(constrained) Pareto efficient whenever there is imperfect and asymmetric
information or when risk markets are incomplete—which is always the case
(see Greenwald and Stiglitz 1986, 1988). Both before and after that paper,
there have been a large number of studies showing that even with rational
expectations, markets are not in general constrained Pareto efficient. Much of
modern macroeconomics forgot these insights, and constructed models
centering around special cases where market inefficiencies do not arise, and
where the scope for welfare-enhancing government intervention, either to
prevent a crisis or to accelerate a recovery, is accordingly limited. This has
made the models of limited relevance either for prediction, explanation, or
policy—at least in times of severe downturns, when markets evidently are
working so poorly.
Prediction is the test of a scientific theory. But when subject to the most
important test—the one whose results we really cared about—the standard
macroeconomic models failed miserably. Those relying on the Standard Model
did not predict the crisis; and even after the bubble broke, the Fed Chairman
argued that its effects would be contained.2 They were not. In the months that
followed, policymakers floundered—and the Standard Model provided little
guidance as to what they should do, for example the best way to recapitalize
the banks. Many of the critical policies before, during, and after the crisis were
based on analyses of modern macroeconomics. Monetary authorities allowed
bubbles to grow, partly because the Standard Models said there couldn't be
bubbles. They focused on keeping inflation low, partly because the Standard
Model suggested that low inflation was necessary and almost sufficient for
efficiency and growth. They focused on nth-order distortions arising from price
misalignments that might result from inflation, ignoring the far larger losses that
result (and have repeatedly resulted) from financial crises. Belief in the
efficiency of the market discouraged the use of the full panoply of instruments
(for example, restrictions on mortgage lending) at the disposal of central banks
and regulators; these would at least have dampened the bubble and mitigated
its consequences. Instead, it was repeatedly claimed that it would be cheaper to
clean up the aftermath of any bubble that might exist than to interfere with the
wonders of the market. Thus, while financial markets and regulators have been
widely blamed for the crisis, some of the blame clearly rests with the economic
doctrines on which they came to rely (Stiglitz 2010a).
There are some, such as Ben Bernanke (2010), who take a markedly different
view, arguing that economic science did not do a bad job. The fault, he argued,
lay not with economic science, but with economic management. I believe he is
wrong—if by economic science we mean the central macroeconomic models
that have played key roles in the formulation of economic policy and thinking in
recent years. He is right, of course, that there were many mistakes in the
application of economic science. Economic policymakers should have been
aware, for instance, of the consequences of the perverse incentive structures
that had become prevalent within the financial sector. But the standard
macroeconomic models neither incorporated them nor provided an explanation
for why such incentive structures would become prevalent—and these failures
are failures of economic science.3
Of course, there is enormous diversity within economics, and even within the
subfield of macroeconomics. Some macroeconomists warned of the looming
bubble and the consequences that would follow upon its bursting. Economists
like Minsky, who warned of the dangers of credit cycles (1992) or Kindleberger
(1978), who described repeated patterns of manias, panics, and crashes, have
come back into fashion (see also Reinhart and Rogoff 2009). Still, there was a
single model, albeit with many variations, that came to dominate, sometimes
referred to as the DSGE (dynamic, stochastic, general equilibrium) model.4 At
the risk of considerable oversimplification, we refer to that model, and the
standard policy prescriptions that were associated with it, as the Standard
Model, or the Conventional Wisdom (CW). As in other areas, such
simplifications help to clarify what is at issue.
Some advocates of that model recognize its limitations, arguing that it is,
however, just the beginning of a research strategy that will, over time, bring in
more and more of the relevant complexities of the world. Anything left out—
agency problems, financial constraints, and so forth—will eventually be
incorporated. I will argue, to the contrary, that that model is not a good starting
point. Such Ptolemaic exercises in economics will be no more successful than
they were in astronomy in dealing with the facts of the Copernican revolution.
Section 2 lays out in broad terms the failures of the Standard Model, while
Section 3 develops the economics of deep downturns, arguing that the major
disturbances giving rise to such downturns are endogenous, not exogenous;
this crisis is not just an accident, the result of an unusually large epsilon, but is
man-made. I explain why economic systems often amplify shocks and why
recoveries are sometimes so slow. Section 4 argues that there have been
systemic changes to the structure of the economy—changes that, within the
Standard Model, should have led to enhanced stability, but which in fact made
the economy more vulnerable to precisely the kind of crisis that has occurred.
Section 5 contrasts the policy implications of our framework with that of the
Standard Model.
Jump to…
2. The Failure of Economic Science and
Alternative Approaches
Any model is an idealization, an abstraction. The central challenge of
macroeconomics is to identify the salient aspects of the economy that help us
explain what it is that we want to explain. And that, of course, is where macroeconomists begin to differ. What is it that they seek to explain? And to what use
do they want to put the model? Because models can be used for different
purposes, it makes little sense to strive for a single model. Yet, to a large extent,
the single model upon which much of macroeconomics focuses is ill-suited for
most of the purposes for which one might hope that such a model might be
used. It was of limited usefulness either for short-run prediction, ex post
interpretation, or the design of policies to prevent fluctuations, to minimize their
scale, or to respond once they occurred. In this paper, I am especially
concerned with deep downturns, such as the Great Recession or the Great
Depression.
Economic Theory as Blinders. Models by their nature are like blinders. In
leaving out certain things, they focus our attention on other things. They provide
a frame through which we see the world. Psychologists have explained how we
discount information that is contrary to our cognitive frame. The result is that
there can be equilibrium fictions—given the information that individuals actually
process, the world as they see it supports their beliefs. For those believing in
perfect markets, even repeated crises are seen as rare events, accidents that
don't really need to be explained (see, for example, Greif and Tabellini (2010)
and Hoff and Stiglitz (2010). Shiller (2008) talks about social contagion.)
The neoclassical investment function provides example of how theory can lead
modeling in the wrong direction. (As is typically the case, the problem lies not
with “theory” but with a specific theory.) For a long time, economists dismissed
incorporating cash flow effects into investment functions, even though empirical
studies (such as Kuh and Meyer 1957) suggested that they should be, because,
it was said, economic theory said that they such effects shouldn't exist. But,
only a little later, economic theories that took into account capital market
constraints arising from imperfect information explained why such effects should
be important, at least at certain times. A vast subsequent literature established
empirically the importance of these constraints (see Gilchrist and Himmelberg
1995).
Trade-offs in Modeling. Because any model is a simplification, an idealization,
of reality, it is not a criticism to suggest that some aspect of reality has been left
out. But it is a criticism if what is left out is essential to understanding the
problem at hand, including the policy responses. If one is interested, for
instance, in understanding unemployment, it makes little sense to begin with a
model that assumes that the labor market clears.
In illuminating some questions, a model of two or three periods may have to
suffice—not because we believe that the world only lasts for three periods, but
because the complexities resulting from the infinite extension preclude
incorporating more important complexities. There are trade-offs in modeling just
as there are in economics.
For instance, it has become acceptable, even fashionable, to use particular
parameterizations, for example, constant elasticity utility functions, often of the
Dixit–Stiglitz (1977) variety, and Cobb–Douglas production functions. In using
them, we should be aware not only of their special nature, but that they have
empirical predictions that can be (and typically are) refuted. For some purposes
(such as the analysis of behavior towards risk), these utility functions provide a
bad description, and one should use such models with extreme caution. When
Dixit and I used the particular utility function that has become fashionable, we
chose it because it provided the benchmark case where markets traded off
optimal diversity and firm scale. The diversity/quantity tradeoff was, we thought,
the fundamental tradeoff in the theory of monopolistic competition, and the
partial equilibrium models that had been at the center of the theory of
monopolistic competition until then simply could not even address this issue.
We would never have thought to have concluded using that model that markets
were on average or in general efficient. Rather, a better interpretation was that
the market was almost surely inefficient; the direction of bias was a subject of
some complexity, though our model provided a framework within which one
could address that issue.
By the same token, if the distribution of income (say between labor and capital)
matters, for example, for aggregate demand and therefore for employment and
output, then using an aggregate Cobb–Douglas production function which, with
competition, implies that the share of labor is fixed, is not going to be helpful.
The large changes in the share of labor imply, of course, that such a model
does not provide a good description of what has happened.
If economics is the science of scarcity, economic modeling is the art and
science of selecting which among the many economic complexities to
incorporate. The question, then, is have the Standard Models focused on what
is of critical importance, e.g. for purposes of predicting the length and depth of
the current downturn in employment or output or the design of the policy
responses?
2.1. The Representative Agent Model
While modern macroeconomics has gone well beyond the representative agent
model, that model has helped shape the direction of research. It is important to
understand its major limitations, and to assess the extent to which more recent
developments, for example in New Keynesian DSGE models, have failed to
come to terms with these.
Methodological Missteps. The Standard Model takes as its methodological
foundation that macroeconomic behavior has to be derivable from underlying
microeconomic foundations. That proposition seems on the face of it
uncontroversial. But it was important that macroeconomics be based on the
right microeconomic assumptions, those consistent with actual behavior, taking
into account information asymmetries and market imperfections. Yet, in carrying
out that research agenda, particular microeconomic foundations (competitive
equilibrium, rational expectations, and so forth) were employed, and, to make
the analysis tractable, particular parameterizations, which in fact are
inconsistent with microeconomic evidence, were used (see Greenwald and
Stiglitz 1987).
The timing of the new classical revolution (which in turn led to the Standard
Models currently in use) was unfortunate. The well-established microfoundations of the standard competitive equilibrium model were just being
undermined by advances in the economics of information and game theory, but
it was to the “perfect markets” models that they turned to provide their microfoundations. The problem is that with perfectly functioning markets we would
not expect to see the kinds of fluctuations that we see—and that we seek to
explain.
With information asymmetries, markets behave markedly differently than they
do with perfect information: markets may not clear; there can be credit and
equity rationing, or unemployment (for a survey, see Stiglitz 2002). Imperfect
information leads to imperfect risk markets, and the two together alter the
behavior of product, labor, and capital markets, and firms and other agents
operating in those markets in fundamental ways that have macroeconomic
implications.
Ironically, the standard paradigm always claimed more “virtue” than it deserved.
For instance, in the absence of the representative agent assumption (all
individuals are identical) virtually any aggregate function can be consistent with
the standard competitive model (Sonnenschein 1972; Mantel 1974; Debreu
1974; Kirman 1992). And when it comes to a key piece of the macro-model—
money and finance—the analysis is ad hoc and hard to justify in terms of
reasonable first principles. Money, for instance, is not needed for most
transactions; credit can be used. With fluctuations in the supply of credit being
at the center of many economic fluctuations, a theory that has little to say about
credit and its determinants is obviously of limited use. Ad hocery was
introduced in other ways as well: the shocks to the economy (typically modeled
as productivity shocks) were simply assumed exogenous. Even if disturbances
to productivity were the major causes of economic fluctuations, surely they are
related to investments in R&D, which should be modeled as endogenous. But
more fundamentally, most of the important shocks to the economic system—
including those leading up to the current crisis—are endogenous. The subprime
mortgage crisis was man-made. To assume that it was exogenous is both
wrong, and obviates one of the major objectives of economic and policy
analysis—to prevent the occurrence of such shocks.
The standard paradigm claimed more virtue than it deserved in another respect:
one advantage of the rational expectations hypothesis is that it helps immunize
the models against the Lucas critique, which emphasized that behavior itself
was endogenous to policy. But behavior is also sensitive to expectations about
policy change. Typically, one looks at whether behavior is consistent with a
given policy regime. But policies change—indeed, one of the points of
macroeconomic analysis is to consider the consequences of policy changes;
and one of the arguments for democracy is that citizens have the right to
change governments, leading to policy changes. To varying degrees,
individuals anticipate these policy changes. Observed behavior, thus, is not just
a function of current policies but about beliefs about what future policies might
look like. A full rational expectations model would have to embrace some kind
of objective probabilities of those changes; conceptually, it is not clear from
where these would come; practically, it is obvious that individuals differ in these
judgments.
Moreover, the Standard Models treat the government as outside the system:5 it
too is modeled as if it were exogenous. But in fact, political actors are agents,
who may use policies to pursue their objectives. They are engaged in a
stochastic game with market participants, and this too should have been
formally modeled (see Korinek and Stiglitz 2008, 2009). Such stochastic games
provide a framework in which we can formally model market agents’
expectations about policy changes. What they make clear is that behavior at
time t can be highly dependent not just on policies at time t, but on beliefs about
policies which might exist at all later dates. A full rational expectations
equilibrium ought to incorporate these; there is considerable ad hocery in
deciding which aspects of the broader socio-political-economic system to
embrace within the rational expectations equilibrium.
The prevailing methodology was, moreover, dominated by as if modeling: True,
most individuals may not be able to solve complex intertemporal optimization
problems of the kind that they are assumed to solve in the Standard Model, but
they behave as if they do, and that is all that counts. Yet, it is all the predictions
of the model that need to be tested. And many of the predictions of the model—
such as those concerning the microeconomic behavior of the constituents—are
inconsistent with the empirical evidence. Attention was centered on certain facts
that seemed consistent with the theory, but those that were not were
conveniently ignored.6 In the end, the macroeconomics is evidently not truly
ground on microeconomics. For instance, the large variations in employment
with little changes in real wages suggest a highly elastic labor supply (if one
assumes that there is no unemployment and that individuals are therefore on
their labor supply function), yet micro-studies suggest highly inelastic labor
supply functions.7 Some of these inconsistencies arise from the use of
parameterizations designed to make the models tractable, but they are not only
implausible—there is ample evidence that they generate behavior that is
inconsistent with what is observed. For example, many models employ
constant-elasticity separable utility functions, implying that all individuals buy
the same portfolio of assets; richer individuals buy the same portfolio that
poorer individuals do.
Why the Representative Agent Models Had to Fail. The major deficiency in the
representative agent model is that there can be no (meaningful) information
asymmetries (at least without the representative agent facing acute
schizophrenia, which was inconsistent with the assumption of unparalleled
rationality); no financial markets (who is lending to whom?); no scope
accordingly for excess indebtedness (who owes money to whom?) or for
deleveraging (who is reducing their indebtedness to whom?); no problem of
debt restructuring; no meaningful capital structures (since the single individual is
bearing all the risk, it is obvious that nothing can depend on whether finance is
provided in the form of debt or equity); no role for bankruptcy; no agency
problems; no externalities. Because there are no agency problems, there is no
scope for problems of corporate governance. Because there can be no
externalities, there is no role for government intervention to align social and
private interests. Because there are no distributive issues, there is no scope for
exploitation—for example by the banks of uninformed borrowers. Changes in
wages and interest rates can have large distributive effects, and therefore large
macroeconomic consequences; but not in the representative agent model: for
instance, what the worker loses through lower wages, he gets back in his role
as “owner” through higher profits. In short, the assumptions underlying the
representative agent model bias the results: there is little scope for the kinds of
market failures that require government action; and redistributive policies that
might affect aggregate demand can't in these models.
2.2. Limitations of the Representative Agent Model and
its Descendants
Repeatedly in history, there have been booms and busts, bubbles that broke, of
the kind that occurred in 2008. They involved breakdowns in financial markets.
Booms typically were marked by excess leverage. Resolving the crises entailed
deleveraging. The inability of the representative agent model to incorporate
meaningful information asymmetries and financial constraints makes the model
of particularly limited use in understanding such fluctuations. By the same
token, the issues that are center stage in this crisis are of no moment. The
major complaint about bank bailouts, that they redistribute money from
taxpayers to bank bondholders and shareholders, is of little concern, because
the representative agent gains as owner of the bank what he loses as a
taxpayer; there would be no impediments to restructuring mortgages, because
what the bank loses in the restructuring the homeowner-cum-bank owner gets
back. Not even unemployment is of much concern: the representative agent
may change the number of hours he works, but only by a small amount, and,
with perfect capital markets, there is little effect on consumption, since the
impact can be easily smoothed out over time (Lucas 1987). Variations in the
demand for labor are of such concern because of how the burden is
distributed—a relatively small percentage are unable to sell all the labor that
they would like, and this can impose enormous hardship on them. Private
insurance markets do not spread this risk. In the Standard Models (with no
information asymmetries) there is no explanation for the absence of these key
markets—like so many other aspects of those models, the absence is just ad
hoc. The models are immune from the Lucas critique only by assumption—that
there is no impact of policy on risk sharing because there is no one with whom
to share risks.
In the following sections, I want to elaborate on the major deficiencies—
assumptions that were included that shouldn't have been and simplifications
that make the model of limited use in studying these fluctuations.
Distribution Matters. As we have noted, it is inequality in the distribution of
work—the fact that some individuals are fully employed while many cannot get
the work they seek—that we seek to understand. As the economy expands and
contracts and the demand for labor increases and decreases, we want to know
(and be able to predict) how those changes are reflected in hours worked per
worker versus the number of workers. In downturns and recessions, the focus
rightly is on aggregate demand, which can be affected in fundamental ways by
the distribution of income. With individuals differing in their marginal
propensities to consume, aggregate savings (consumption) rates depend not
just on income, but on its distribution.
Many interpretations of the current crisis have emphasized the importance of
distributional concerns. The growing inequality (which itself should be explained
within the model) would have led to lower consumption but for the effects of
loose monetary policy and lax regulations, which led to a housing bubble and a
consumption boom. It was, in short, only growing debt that allowed
consumption to be sustained. But with the breaking of the bubble, even if banks
were fully functional, the level of indebtedness—and the levels of
consumption—that prevailed before the crisis can't be sustained.
Prior to the crisis, some analysts looked at the average equity of homeowners
in their home. Even a marked decline in housing prices would, in these
calculations, leave significantly positive average home equity. But again,
distribution matters: what was of concern were the large numbers of
homeowners who had sufficiently little equity that, say, a 20% or 30% decline in
home prices would leave them underwater, and therefore at risk of foreclosure.
Distributional conflicts are at the center of the impasse in dealing with the
mortgage crisis. As we note later, the critical question is, who bears the losses?
Distributional impacts are at the center of many other policy choices. Lifecycle is
central to behavior; yet models with infinitely lived individuals have no lifecycle.
When interest rates are lowered to near zero, policymakers should worry about
the plight of risk-averse elderly, who have much of their savings in short term Tbills. But even if one were to focus only on aggregate demand, these
distributional effects can be of first-order importance: If the interest elasticity of
investment is low, the increased investment may be lower than the decreased
consumption of the elderly. Lowering interest rates may then actually weaken
aggregate demand. When the impact of savings of those approaching
retirement is considered, matters could be even worse: these individuals might
increase their savings rate, to make up for the low return.
Price changes always have distributional effects (outside of the representative
agent model), and it is only under highly special cases that the effects of the
winners just offset those of the losers. Indeed, unexpected interest rate or price
changes lead to unexpected gains in some firms’ net worth, matched by losses
to others. But firm investment and supply is in general a concave (non-linear)
function of net worth (taking into account financial constraints that arise
endogenously from information imperfections), so that such changes can, in
general, lower aggregate demand and supply. Indeed, there can accordingly be
adverse effects in the short run both from increases and decreases in prices
(evidenced, for instance, in the adverse effects observed when the price of oil
increased in the 1970s, and decreased in the 1980s; see Greenwald and
Stiglitz 1993).
Interestingly, if we formulated a representative agent model of the world,
changes in exchange rate should make no difference: gains to some are just
offset by losses to others. But almost all economists recognize that exchange
rate changes do matter. The Standard Models assume, implicitly, that
differences between countries matter, differences within countries don’t. But
that is, of course, both ad hoc and wrong.
Markets Are Not Fully Rational. Several critical aspects of the behavior of the
economy seem so patently inconsistent with any model of rationality that
attempting to construct a model predicated on rationality that explains such
behavior is almost doomed from the start. Most neoclassical investment models
entail an analysis of the cost of capital, taking into account given tax structures.
But with full tax deductibility of interest, if marginal investment is financed by
debt, the corporation tax leaves the effective cost of capital unchanged. Most
investment models use instead of a marginal cost of capital a variable more
appropriately interpreted as an average cost, taking into account how
investment on average is financed. But it is hard to reconcile overall corporate
financial policy with any model of rationality: there are ways of distributing funds
from the corporate sector to the household sector that entail the payment of
lower taxes (the dividend paradox).8
In the run-up to the crisis, investors, consumers, banks, and regulators all
exhibited behavior that is hard to reconcile with the hypothesis of rationality as it
is incorporated in most Standard Models. Alan Greenspan's mea culpa put the
matter forcefully:
“[T]hose of us who have looked to the self-interest of lending institutions to
protect shareholders [sic] equity, myself especially, are in a state of shocked
disbelief.”9
Some of the “deviant” behavior can be explained by the incentive structures of
bank managers. They (the decision makers) might have been managing risk in
a way that maximized their own welfare—even if it didn't maximize the welfare
of shareholders, let alone society as a whole. Indeed, given the incentive
structures, we should have been surprised if banks had not undertaken
excessive risk taking. Textbooks would have had to have been rewritten: it
would have meant that, after all, incentives did not matter. Thus, I was surprised
at Greenspan's surprise that banks had not managed their risks better. Some of
the deviant behavior can also be explained by distorted organizational
incentives arising from too-big-to-fail financial institutions. But the Standard
Models didn't incorporate these incentive distortions—and therefore, like
Greenspan, didn't anticipate the problems to which they would give rise.
But the failures run deeper: standard theory argues that markets should be
efficient in their choice of incentive structures. Modern microeconomics (of the
kind not incorporated into modern macroeconomics) explains this failure.10
Macroeconomics based on modern microeconomic foundations would have
gone beyond the simplistic models of firms and finance of the past. Admittedly,
this would have been difficult (though this is the thrust of much of the alternative
macro-developments described later in this paper). But it should be obvious that
we can place little reliance on macroeconomic models based on flawed microfoundations.
Regulators and investors should have recognized (i) the pervasiveness of the
agency problems and the risks to which that exposed them and the economy;
(ii) the peculiarities (and risks) associated with commonly employed incentive
structures—including the incentives for non-transparency and the provision of
distorted information by firms and banks; (iii) the risk associated with increased
leverage, unmatched with any (social) benefits. Their failure to do so and to
take appropriate actions is itself evidence of market irrationality.
While it is amply clear that the neoclassical assumptions underlying the
standard model cannot explain widespread behavior, it is not always evident
which assumption fails; for example, whose irrationality was pivotal. For
instance, under the standard assumptions, the Modigliani–Miller theorem would
hold; indeed the high risk and costs of bankruptcy would exert a strong force
limiting leverage.11 The Modigliani–Miller theorem ceases to hold, of course,
even with rational market participants, in the presence of important agency
problems and other information asymmetries. But it is not clear that most
market participants (including bank management) fully understood the risks
associated with their high leverage—unless, of course, we assume that they
were in fact counting on some form of bailout, a hidden or open transfer to them
from the taxpayer. In retrospect, such expectations appear to be rational, and it
was the regulators who ignored this that were irrational. By the same token,
many of the banks might have been rational in exploiting uninformed borrowers;
but it is hard to believe that many of those taking out some of the worst forms of
subprime mortgages were rational. Many of those in the financial sector had an
irrational optimism about the ability of poor borrowers to repay.12 In short, even
were we to pin blame for the crisis—and for the failure in our models—on
irrationalities, there is a question of whose irrationality?
Recent years have seen the development of behavioral economics, and its
application to macroeconomics (Akerlof 2002; for an interpretation of the crisis,
see Fuster et al. 2010). There are many important and systematic aspects of
behavior that simply can't be reconciled with the standard utility-maximizing
model, and increasingly, these have come to play a role even in policy (for
example in the form in which taxes were reduced in the Obama stimulus
package.)
The Limitations of Rational Expectations. The hypothesis of rational
expectations which has played such an important role in modern
macroeconomics is questionable in general13 and of little applicability in the
current situation. There hasn't been a crisis as deep as the current one for
three-quarters of a century, so how can market participants form rational
expectations about how modern economies respond to such a situation, unless
they make the leap of faith that responses to a large crisis are similar to
responses to smaller perturbations? How can a retired person, who has relied
on interest payments from government bonds, form rational expectations about
future interest rates when they have never been so low? There is no simple
empirical evidence on the basis of which he can meaningfully extrapolate what
will happen.14 The standard rational expectations models not only assume that
they can do so, but that all market participants have the same (rational)
expectations. Yet there is little reason to believe that they will formulate the
same model—or that the models they formulate, even on average, are correct;
and it is differences in beliefs that drive much of what happens in the
economy.15
The problem for the government is even more difficult. For it must base its
policies on beliefs about how economic agents are behaving, who in turn must
base their behavior on beliefs about how the government is acting. They all
have remarkably solved instantaneously for the fixed point—in a context which
has never previously occurred! And again, the rational expectations hypothesis
is of limited relevance in assessing the consequences of policies that have
never (or almost never) been tried before, at least in comparable
circumstances.16 Describing and analyzing the full rational expectations
equilibrium is complicated enough; to presume that in a rare event such as the
current one that all economic and political participants have quickly gravitated to
this equilibrium is beyond credence.
Moreover, even if expectations on average were rational, in the presence of
financial constraints, market behavior might be markedly different from what
would occur if there were a single individual with rational expectations. Consider
quantitative easing. It may increase anxieties about future inflation, and if that
happens, then long-term interest rates may rise. While an increase in
inflationary expectations in excess of the increase in interest rates lowers real
interest rates, it may not increase investment: Firms, sensitive to the demands
on current cash flows that the higher (nominal) interest rates entail, may curtail
investment.
Modern macroeconomics prides itself in combining theory with rigorous
empirical work. But all policy analyses are predicated on the belief that data
from earlier periods are relevant to the current experience. But whether that is
the case is an article of faith—one which may make sense when “today” looks
much like earlier periods. But the world today looks markedly different. The hard
question is, what aspects of behavior carry over? Are empirical results
describing firm behavior when excess capacity is low still relevant when excess
capacity is at record levels and expected to be for some time? Do empirical
results concerning consumer behavior when indebtedness is low still apply
when indebtedness is high? We might try to make inferences by looking at the
behavior, in more normal times, of firms with high excess capacity or
households with high indebtedness. But almost by definition, such firms and
households are then“outliers”; we should be cautious in making inferences
about the behavior of ordinary firms and ordinary households in these unusual
circumstances from observing behavior of outliers in normal times.
Market Clearing. In forming expectations, for most workers, more important
than future wages and prices is the risk of unemployment; but obviously, such
expectations can play no role in a real business cycle model in which the labor
market is assumed to clear. Today, for most households, a key question in the
solution to their dynamic maximization problem is whether they will be able to
refinance their mortgage and if so, at what terms—financial variables that are
more relevant than the interest rate at which the government can borrow. For
SMEs, the availability of finance is as or more important than the interest rate.
In the Standard Model, these questions simply don't arise, because markets are
assumed to clear. Efficiency wage theory has, for instance, provided rigorous
microfoundations explaining why labor markets may not clear (Shapiro and
Stiglitz 1984; Rey and Stiglitz 1996).
One of the hardest analytic questions is trying to understand which results can
be attributed to which assumption. For instance, the result in many variants of
rational expectations models, that government policies were ineffective, was
attributed to the assumption of rational expectations (with private agents
offsetting government actions). But this was wrong. If goods and labor markets
don't clear, say because of wage and price rigidities, and individuals have
rational expectations, then not only is government fiscal policy effective, but
multipliers are greater than without rational expectations, as consumers
respond to rationally expected increased incomes in future periods by increased
consumption today (Neary and Stiglitz 1983).
Institutions Matter. The Standard Model assumed that institutions don't matter;
but institutional details are often of first-order importance. For instance, to
understand mortgage default rates (and the difference between patterns in the
United States and some other countries) one has to note that first mortgages in
the United States are typically nonrecourse, while those in other countries are
not. To understand the difficulties of restructuring (which would seem, in most
instances, to be Pareto superior to current practices which often lead to costly
foreclosure proceedings and incentives to trash the homes in the process) one
has to understand the conflicts of interest that arise between the first and
second mortgage holder and the service provider. A model whose structure
ignores these issues will give too much credence to the ability of the markets to
work everything out for the best, and provide little guidance to what government
might or should do.
But even more fundamentally, the Standard Models left out both banks and the
shadow banking system, central to the determination of the flow of credit, which
in turn is central to the determination of aggregate demand.
2.3. Dynamic Stochastic General Equilibrium Models
Some of the problems that I have outlined have been recognized.
Macroeconomists have gone well beyond the representative agent in the DSGE
models—especially in the variant known as the New Keynesian DSGE
models—that have become so popular. While some of these models have
attempted to address some of these concerns, the Ptolemaic approach of
attempting to refine a fundamentally flawed model is not, I think, the most
promising approach for helping us to understand these issues. Introducing
monopolistic competition and nominal rigidities means the market equilibrium is
not, in general, Pareto efficient, monetary policy can have real effects, and,
more broadly, government intervention can be welfare enhancing (see for
example Galí 2008). Still, advocates of the various variants of DSGE have
themselves emphasized the overall similarities.17 As Chari et al. (2009) note, the
various versions of the DSGE models even agree on the central policy: “optimal
monetary policy…keep[s] inflation low and stable in order to avoid sectoral
misallocations” (p. 246). But the social costs of these misallocations are nth
order compared to those from disruptions in the financial sector. Indeed, they
may be even small compared to those arising from changes in relative prices
that result from differences in the short-run price determination processes
across sectors (in some markets, prices are set by firms while in others, prices
are set by, in effect, auction, Stiglitz 1996a)—effects which were ignored by
assumption in virtually all of the DSGE models. Thus, most of the key criticisms
leveled against the Representative Agent model are, for the most part, still valid
in the DSGE models of whatever variant: Financial sectors are not well
modeled, including the banking and shadow banking sectors and the links
between monetary policy and credit. Levels of aggregation (key to policy
analyses) are similar. Key assumptions, such as market clearing (no credit
rationing), rationality, and rational expectations are retained.
Also, as I have noted earlier, many results are implicitly due to particular hardto-defend (other than as matters of convenience) parameterizations, and many
at the various variants of DSGE have continued to employ the same
parameterizations. Earlier, I explained how Cobb–Douglas production functions
rule out changes in factor distribution, which can be important in determining
aggregate demand. But there are other objections: Even if there is unitary
elasticity of substitution ex ante (before the capital good are constructed), there
is not ex post.
Seemingly, the one thing that DSGE models seem to have in common is agents
that maximize intertemporal utility—often with separable utility functions (though
this too is presumably simply an assumption of convenience). The focus of
dynamics is on intertemporal substitution effects, mediated through interest
rates. There are good grounds for questioning the significance of these effects
for investment or consumption, at least in response to the kinds of variations in
interest rates normally observed. Indeed, for long periods of time, real interest
rates were approximately constant—making it hard to believe that they were an
important channel by which policy affected behavior. It takes considerable
massaging of the data to ensure that investment is sensitive to real interest
rates and not nominal interest rates. Short-run fluctuations especially are as or
more dominated by credit availability, changes in firm or bank equity, and
government expenditure shocks.
But even if behavior is significantly affected by interest rates, there are large
disparities between lending and borrowing rates (bank deposit rates are close
to zero, consumer lending rates through credit cards close to 30%), and
between T-bill rates and these rates. What matters is not the interest rate(s) at
which the government can borrow, but those at which firms can borrow—if they
can get access to funds; the spread between the two is an endogenous
variable, that has to be explained. While monetary policy shocks seem to have
real effects, and may be reflected in changes in nominal (and/or real) interest
rates, that by itself does not necessarily mean that the interest rate effects
dominate consumer or firm responses. Rather, the effects may be mediated
through banks. Banks can, for instance, raise interest rates and reduce credit
availability in response to tightening of credit by the Fed. The observed
correlations between interest rates and investment are reduced-form
relationships, not structural relationships.
A commitment to the Standard Model with its aggregate production function
forces one to conclude that, with real interest rates negative at the current time,
in the midst of the economic downturn of the Great Recession, the marginal
productivity of capital has suddenly become negative (hard to reconcile with any
of the standard aggregate production functions); or else to explain the
discrepancy between the negative real interest rate and the seeming positive
returns to capital through the imposition of some arbitrary “wedge” in the
equilibrium condition. Neither approach is plausible or persuasive. (Note the
marked difference between the Great Depression and the Great Recession. In
the former, there was worry that a liquidity trap would prevent nominal interest
rates from falling to zero, and even if they were very low, rapidly falling prices
meant high real interest rates; in the Great Recession, nominal T-bill rates have
been brought down close to zero, and prices have been rising.)
It is a positive development that certain imperfections are being introduced into
the New Keynesian DSGE models, but how imperfections are introduced
matters: not even the advocates of labor market frictions based on “search”
believe it can explain current levels of cyclical unemployment. While agencybased theories of credit imperfections are a marked improvement over models
with perfect capital markets, they suggest that markets would have shown more
restraint in bank leverage and risk-taking than was observed.
The extension of DSGE models to make them more realistic, more consistent
with macroeconomic data, has come with a price; as critics of New Keynesian
DSGE models, like Chari et al. (2009) point out, the improved macroperformance is accompanied by an increased arbitrariness at least in certain
specifications, including alleged “structural shocks” (say wedges between the
marginal rate of substitution and marginal rate of transformation), which may
make them not immune from the Lucas critique—the size of the wedges might
be affected by the policies themselves. Consider, for instance, the puzzle
alluded to earlier, concerning labor supply. To make the models consistent with
observed behavior, one can either postulate shocks to consumers’ preference
for leisure or to workers’ bargaining power. In one view, markets are efficient,
and it is left to psychoanalysis to explain why workers who have decided to
enjoy more leisure seem so unhappy. In New Keynesian versions, it is tempting
to attribute the outcomes to wage rigidity (increased union power), with obvious
implications for the desirability of union busting. But it is more plausible that the
wedge between the marginal rate of substitution and marginal rate of
transformation changes for other reasons; it is endogenous, and needs to be
explained. For instance, changes in the wedge over the cycle may be
endogenously generated by (i) decentralized processes of wage and price
adjustments (Solow and Stiglitz 1968); or (ii) by changes in the effective
(shadow) real interest rate—taking into account financial constraints—in
intertemporal models with monopolistic and monopsonistic competition with
endogenous mark-ups (not the fixed mark-ups assumed in the Dixit–Stiglitz
model).18 Cyclical movements in (shadow) real interest rates, in turn, are related
inter alia, to changes in firm and bank equity positions. Thus, the 1991 and
2008 US downturns differ from other post-war downturns, in the large losses in
the capital of many banks.
A central thesis of this paper is that the DSGE models have made the wrong
trade-offs, focusing on some complexities which are of less importance than
those that they ignore (and in some cases employing assumptions that are
implausible). For instance, we noted earlier that the complexities of lifetime
utility maximization typically forces modelers to employ parameterizations, the
implications of which can be rejected. There are marked differences between
models with infinitely lived individuals and overlapping generations models—
with the latter arguably providing a better description of most households
(Benassy 2007). Dynamics are important, but the dynamic effects that were
included (arising, say, from intertemporal maximization of an infinitely lived
individual) are less important than the dynamics that were excluded.
Jump to…
3. Towards an Economics of Deep Downturns
Part of the problem of modern macroeconomics is that it focused on explaining
better the small and relatively unimportant fluctuations that occur “normally”,
ignoring the large fluctuations that have episodically afflicted countries all over
the world. The fact that a model may do slightly better than straightforward
extrapolation in predicting growth rates at t+1, say from the vantage point of t, is
of little moment: the welfare loss from a typical error in prediction in normal
times is small in comparison to that associated with the failure to anticipate a
crisis, the effects of which can persist for years, during which the economy
operates well below its potential.
It was as if we had developed a medical science that could treat individuals’
colds, but had nothing to say about serious illnesses. A doctor that said that that
was good enough, because most of the time individuals were either healthy or
suffering from the sniffles, would not be taken seriously; but that was the
position taken by much of mainstream economics. Indeed, in medicine, one
learns much about the human body in normal times by studying pathology—
what happens when things don't work normally. So too, economists should be
learning from the “pathology” of recessions and crises. These are the instances
where market inefficiencies cannot be ignored; but these inefficiencies are the
tip of the iceberg; beneath are pervasive but sometimes hard-to-detect market
failures.
The point may be made in another way: Consider the difference between
modern physics and modern macroeconomics. Black holes have played a
central role in the development of modern physics. Of course, they “normally”
don't occur. If methodologies analogous to those that prevailed in economics
had dominated in physics, black holes would have been dismissed as an
irrelevant exception.
What is to be Explained? There are many macroeconomic variables that may
be of interest for one reason or another. In the long run, growth matters. In the
1970s, it was understandable that inflation should be the object of concern.
Today, as in the Great Depression, it should be deep downturns that should be
the focus of attention. Of particular concern is unemployment. Persistent
unemployment is, of course, a sign of an important market failure. It represents
a massive waste of resources. There is ample evidence too that unemployment
gives rise to a loss of well-being that is far in excess of the loss in income, with
enormous social consequences (Fitoussi et al. 2010). Any model worth its salt
has to be able to explain and predict movements in unemployment. In doing so,
a model that assumes that labor markets clear will be of little help; nor will
models that simply assume that unemployment arises from arbitrarily specified
wage rigidities. Such an assumption pre-ordains the solution: get rid of the
wage rigidity. Moreover, such an explanation is suspect: in the Great
Depression, wages fell a great deal—they could hardly be called rigid. And in
the Great Recession, the United States has been plagued by high
unemployment (with one out six workers who would like to get a full-time job not
being able to get one), even though it has claimed to have had one of the most
flexible labor markets, and has the weakest unions, among the advanced
industrial countries.
Credit. This paper focuses on deep downturns; and it is only by understanding
credit—how the supply of credit is determined, why at times there can be
excessive credit, while at other times the supply of credit can collapse—can we
understand this and many of the other major fluctuations that have plagued
capitalist economies over the past two hundred years. Even before this crisis,
Greenwald and I had argued that understanding changes in the supply and
demand for credit was at the heart of understanding economic fluctuations
(2003a), and understanding how monetary policy (both convention instruments
as well as regulatory instruments) affects the supply of credit should be at the
heart of monetary theory. In normal times, money and credit may be highly
correlated, and so data on money supply may do as a surrogate for credit. But
in times of crisis, such as the current one or the East Asia crisis, the link is
broken. Moreover, secular changes in our financial system can change the
linkages between money and credit, and thereby affect how monetary policy
works.
This crisis is typically traced to the disappearance of credit after the bursting of
the real estate bubble, and especially after Lehman Brothers’ collapse. As the
crisis broke, with the credit supply rapidly contracting, Standard Models
focusing on money, not credit, where banks and security markets were not well
analyzed, provided little guidance on how government could restore the flow of
credit. That—not interest rates—was the central issue.19 The resulting failure to
resuscitate lending should thus not come as a surprise.
Money and Credit. Unless we understand the relationship between monetary
policy (broadly defined, to include regulatory instruments) and credit, we won't
understand the role of monetary policy in responding to credit crises.
Summarizing the financial sector into a money demand equation simply won't
do, nor will focusing just on interest rates. Even while some of the Central
Bankers admit that their ability to resuscitate the economy is limited, they
continue to believe that monetary policy can have some effects on real interest
rates, which in turn will have some effects on real activity. Typically, they use
money demand equations, implicitly based on a transactions demand for
money. But today, money is needed for relatively few transactions—credit is all
that is required. In the absence of financial market constraints (arising from
imperfect information) financial policy (for example maturity structure of debt,
quantitative easing) would matter little, if at all (see Stiglitz 1981, Stiglitz (1983,
1988; Greenwald and Stiglitz 2003a). One might, of course, justify the modeling
of money demand on the grounds that it is a good reduced-form
approximation—it works well (except when it doesn’t). Yet, such an ad hoc
justification is totally out of the spirit of DSGE modeling, which prides itself on
deriving all of the relevant behavioral relationships from more basic primitives
(like utility functions and production functions).
Indeed, within the standard model, it would be hard to make sense of much of
what has occurred in recent years. We have already noted the seeming
irrationality of banks’ demand for leverage. Collateral-based lending has played
an important role in generating the bubble, and in the bust that followed. But the
very practice of requiring collateral only arises because of financial market
constraints (and differences in judgments of the likelihood of occurrence of
different events, leading to the importance of control). In a neoclassical model,
there would be no reason for an individual to borrow, posting an asset as
collateral, rather than simply selling the asset (reducing the amount he has to
borrow.)
As the crisis has evolved, much has been made of the distinction between
insolvency and illiquidity. But if everyone shared the same (rational expectation)
beliefs, an individual who is solvent, who, with probability one, could more than
meet his debt obligations could get access to funds.20
In the analysis of deep downturns that follows, we focus on three questions:
Why have they occurred? Why do disturbances get amplified? And why are
recoveries so slow?
3.1. Bubbles: Explaining the Origins of Fluctuations
The literature during the past couple of decades that has gone under the rubric
of business cycle began from the presumption that the origin of fluctuations was
exogenous. There were technology shocks. A wave of Alzheimer's disease
passed through the economy in 1929, leading to a regression in technology, to
which the economy adjusted.
It is hard to explain in a plausible manner this crisis—or most other major
downturns—in terms of exogenous shocks to an economy which in the absence
of such shocks would have grown smoothly.21 This crisis, like most major
preceding ones, is man-made: the economic system itself created a bubble, the
inevitable bursting of which led to the recession. In terms of general economic
theory, there are a variety of conditions under which markets create their own
noise, that is, when the equilibrium, sometimes the only equilibrium, entails
random behavior (mixed strategies) on the part of market participants.22
Macroeconomic structures in which such behavior naturally arises have not
received corresponding attention, with one important exception: the proclivity of
markets to create bubbles and credit cycles. As we noted earlier, economic
historians have noted their repeated occurrence, suggesting that in most
instances they were partly the result of irrational exuberance, often following the
occurrence of a major innovation. Following such innovations, one cannot
simply look to the past to predict the future—rational expectations models are
inherently of limited relevance. In the current crisis, there was the irrational
belief that innovations in financial markets had allowed risk to be much better
managed.
But, regardless of the source of the underlying perturbation (whether
exogenous or endogenous), capital market imperfections impede the ability to
smooth out fluctuations—and may even amplify them. Individuals who believed
that there was a housing bubble had only a limited ability to go short—not
enough to “correct” the prices; and they had to have the wherewithal to maintain
their position for an extended period of time.
Market irrationalities and financial market constraints interact. While one can
construct models with rational expectations with bubbles (Abren and
Brunnermeier 2003) (for example through rational herding, e.g. Banerjee 1992),
probably more important are irrational herding and collateral-based lending.
Individuals, seeing house prices rising, wanted to join the party before it was
over (see also Allen, Morris, and Postlewaite 1993). Even if many rationally
believed that it would end, they irrationally believed that they could win in the
short run, and would outsmart the market, and not be caught in the downdraft.
Collateral-based lending (combined with the difficulties of selling short) meant
that as prices increased, those who were “long” on housing could borrow more,
and take an even bigger position, pushing up prices even further, vindicating
their “wisdom”. For discussions of credit-based bubbles and cycles, see for
instance Kiyotaki and Moore (1997), Miller and Stiglitz (1999, forthcoming),
Minsky (1992).
In fact, even without exogenous shocks, but with financial constraints and
lagged responses, it is easy to construct models with fluctuations. Non-linear
complex models give rise to interesting patterns of dynamics. Even without
exogenous stochastic disturbances there may be oscillations with no regular
periodicity, economic fluctuations, chaotic patterns, where the economy neither
converges nor diverges, but perpetually oscillates.23 In fact, if investment is
limited by profits (there are no capital markets) with plausible wage dynamics
the economy is subject to oscillations (Akerlof and Stiglitz 1969). When wages
are low, profits and investment are high, which leads to a larger demand for
labor; the resulting rising wages then lead to reduced profits and investment,
leading in turn to a lower demand for labor. Were such an economy, buffeted by
shocks, it would not necessarily converge rapidly (or ever) or directly to the new
equilibrium.
3.2. Fast Declines and Amplification
The second major puzzle that has to be explained is why do economic declines
sometimes happen so quickly, and why do what might seem to be small shocks
get amplified? In the absence of war, state variables (capital stocks) change
slowly. Why then can the state of the economy change so quickly? While the
economic system sometimes amplifies shocks, standard theory argues that
economic systems do just the opposite, through several mechanisms. Price
adjustments mean that, say, a shock to the aggregate supply curve results in a
smaller change in output than would occur in their absence. Firms create
buffers, like inventories, to act as shock absorbers. Speculators do research,
anticipating events, putting aside stocks as the probability increases of an
adverse shock in which their value might rise. Understanding amplification—
how small disturbances can give rise to large effects—should be one of the key
objectives of macroeconomic research.
Consider the most recent crisis. Even the major misinvestments in the United
States by the financial markets entailed a loss of, say, somewhere between a
half trillion and two trillion dollars, a small fraction of the global capital stock.
Indeed, the small size—and the belief in the markets’ ability to spread risk
throughout the system—probably accounts for Bernanke's confidence that the
risks were contained. He was, of course, badly wrong, but this belief (shared by
many other policymakers, and consistent with standard macro-models) helps
explain the slowness with which they reacted to the impending crisis.
In the discussion that follows, I describe some aspects of amplification,
especially those arising out of capital constraints, that have been uncovered by
recent research and experience. While price rigidities have long been blamed
for the economy failing to quickly return to full equilibrium after a shock, price
disturbances interacting with financial constraints play an important role in
amplification. Economies with more flexible prices may actually be more
volatile.
Expectations. While the capital stock changes slowly, there can be large and
sudden changes in expectations. Before the bubble broke, large numbers had
seemingly believed that prices of housing would go up indefinitely (or at least
for the foreseeable future); suddenly, the question became only how far down
would they go. But to observe that expectations (unlike other state variables,
like the capital stock) can undergo rapid or discontinuous changes just pushes
the question back further: Why should expectations change so dramatically,
without any big news? And especially with rational individuals forming Bayesian
expectations? Consider, for instance, the puzzle of October, 1987: How could a
quarter of the PDV of the capital stock disappear overnight?
Policy Changes. Another source of large and sudden changes is discrete
government policy changes. Lehman Brothers’ bankruptcy can be thought of as
an example—suddenly an implicit government guarantee was removed.
Similarly, in the last global crisis, there were dramatic increases in interest
rates, with large impacts. But like sudden changes in expectations, these
discrete policy changes usually (though not always) are a result of sudden
changes in state of economy. Though intended to dampen the effects of an
exogenous shock, they sometimes have the opposite effect of amplification.
Structural Non-linearities. Modern mathematical modeling (for example, chaos
theory) has shown that there can be large changes in the state of economy
from small changes in state variables (the butterfly effect). For instance, when
the economy is in some part of the state space, it converges to one equilibrium;
in another part of the state space, to another. If the economy is near the
boundary between one region and the other, a small perturbation can give rise
to large effects. Kirman (2010) has noted that economic systems (viewed as
complex adaptive processes) may exhibit major phase transitions. Nonlinearities have played an important role in traditional business cycles, where a
self-supporting expansion in a standard multiplier–accelerator model was
suddenly brought to an end as the economy reached labor force constraints
(see Goodwin 1951; Kaldor 1951). As these constraints began to bind, growth
slowed, so investment slowed; but that meant that aggregate demand itself
slowed, and the economy went into a downturn.
Financial Constraints. Financial constraints can give rise to both non-linearities
and amplification. Individuals face credit constraints (including borrowing limits
which arise endogenously with imperfect information or restraints on lending
imposed by regulators); and such constraints can lead to the end of bubble.
Regulators in the United States in effect “bent” the standard regulatory
constraints, allowing higher loan-to-value and loan-to-income ratios. This
allowed the bubble to continue longer than it otherwise would have (with the
consequence that the downturn was larger than it otherwise would have been).
But there are limits to such regulatory laxness, and eventually financial
constraints bind. When that happens, housing price increases are basically
limited by the rate of increase of incomes. With most Americans’ incomes
stagnating, in the context of the recent crisis, which meant that prices had to
stagnate. With prices stagnating, the cost of owning a home, this had been in
effect negative (taking into account the expected capital gain) suddenly became
very positive (all events that were not “rationally expected”). The demand for
housing plummeted. Prices fell. The bubble had broken. Financial constraints
meant that the bubble could not have persisted, even with regulatory
forbearance.24
Financial Accelerator. The standard multiplier-accelerator model was predicated
on a fixed capital–output ratio. With Solow's 1956 paper, focus shifted to a
neoclassical production function, and the multiplier–accelerator model grew out
of fashion. Financial constraints give rise to the financial accelerator (derived
from capital market imperfections related to information asymmetries), which
operates in many ways like the old accelerator.25 Imperfect information explains
why it is costly for firms to raise additional equity after a shock which adversely
affects firm equity (Greenwald, Stiglitz, and Weiss 1984; Maljuf and Myers
1984) and why firms’ ability and willingness to borrow is limited by their equity
and the value of their collateralizable assets. If firms can borrow a multiple of
their equity, then a “shock” to equity can give rise to a change in aggregate
demand (through investment) and supply (because of limited working capital)
that is a multiple of the original perturbation.
Pro-cyclical Inventory Movements. Financial constraints lead to amplification
through a number of other channels. For instance, inventories have traditionally
been thought of as buffers, one of the mechanisms by which the economy
absorbs shocks. But in practice, inventories often move pro-cyclically,
contributing to economic volatility. When firms face adverse shocks to net
worth, given equity and credit constraints, they seek to liquefy their assets, so
that they are in a better position to absorb further adverse shocks. One way that
they do this is to reduce inventories. In some cases, they may be forced to do
so to get cash, when access to credit is restricted. The resulting negative
investment weakens the economy further.
Trend Reinforcement through Interest Rate Changes.Battiston et al. (2010)
have drawn attention to a variety of other trend reinforcement effects: a firm that
has a negative equity shock also has to pay higher interest rates, and this
means that the expected return on its equity capital going forward is lower.
Price Changes. Shocks lead directly and indirectly (through expectations) to
price changes. Small shocks can lead to large price changes, which can have
large effects on net worth or the value of collaterizable assets, and then through
the channels described earlier, those changes are further amplified.26
Moreover, redistributions of wealth, generated by price changes, can have firstorder effects, increasing the magnitude of the effects already noted. (Of course,
in a representative agent model, there are no macro-effects from such
redistributions.) In fact, with large price changes, and especially with large
gambles based on those prices, there can be fast redistributions (large balance
sheet effects) with large real consequences, for example if there are large
differences between firms, with some facing financial constraints and others not.
One of the insights of the economics of information is that even a small change
in prices can have first-order effects on welfare (and behavior). A change in
prices can affect the extent to which information constraints (self-selection or
incentive-compatibility) bind. This is, of course, not true in the standard model,
where market equilibrium is Pareto optimal, and small changes have small
welfare effects, by the envelope theorem (see Greenwald and Stiglitz 1986;
Akerlof and Yellen 1985; Stiglitz 2009).
Lending. Banks can be viewed as firms that specialize in lending (assessing
creditworthiness, monitoring, and enforcement). Decreases in bank net worth
can lead to contraction of their lending, with effects that are again a multiple of
the original adverse impact on net worth: the decrease in net worth decreases
the resources available to lend and their willingness to borrow to get additional
resources; moreover, if banks face capital adequacy constraints, the amount
that they can lend is reduced by a multiple, unless they raise additional capital,
which may be especially costly at such times (partially because the need to
raise capital raises questions about the banks’ balance sheet).
Bankruptcy Cascades. The bankruptcy of one firm increases the probability of
bankruptcy of its suppliers and creditors; this can give rise to a bankruptcy
cascade, amplifying the extent of bankruptcy and the systemic costs of the
shock that originally gave rise to the bankruptcy (Allen and Gale 2001;
Greenwald and Stiglitz 2003a, Chapter 7).
Contagion. Bankruptcy cascades are an example of contagion, where a
problem in one country (firm) spreads, like a disease, to others, with effects (if
they are not contained) a multiple of the original disturbance. Contagion is thus
one important source of amplification, and is discussed more fully in Section
5.1.
New Uncertainties. Amplification can also arise through new uncertainties
posed by a shock, especially as it evolves through the economic system: Large
changes in prices lead to large increases in uncertainties about the net worth of
different market participants and hence about their ability to fulfill contracts.
More flexible prices thus can serve to amplify the effect of a shock.27 Changes in
risk perceptions (not just means) matter, given the cost of bankruptcy and the
risk aversion of firms.
There is another, related reason for amplification: A crisis such as the current
one showed that prevailing beliefs might not be correct. Those beliefs had led to
a complacency that risk could be, and had been, effectively diversified, so that
the economy would be able to handle shocks of any size. The crisis quickly
eroded beliefs both in the underlying theories and in the officials responsible for
economic management. Beliefs about the possible depth and duration of the
crisis accordingly could, and did, change dramatically.
Control Changes. There is one more mechanism through which a small
perturbation can lead to large effects, and that is through a sudden change in
control. Who exercises control matters (unlike standard neoclassical model,
where managers simply maximize the value of the firm). The result is that there
can be discrete changes in behavior with changes in control, and with
bankruptcy and redistributions, there can be quick changes in control. In this
crisis, there were many changes in control, but whether these were significant
enough to lead to macroeconomic changes is not clear.
There were strong feedbacks among these elements: For instance, changes in
beliefs led to increased uncertainties directly and indirectly, as they fed into
lower prices for real estate, increasing balance sheet uncertainties. These new
uncertainties in turn contributed to the “bite” of financial constraints (see also
Korinek 2010b, 2011).
3.3. Persistence: Why is Recovery so Slow?
There are large losses associated with misallocation of capital before a bubble
breaks. But most of the losses occur after a bubble breaks, in the persistent gap
between actual and potential output. Standard theory predicts a relatively quick
recovery, as the economy adjusts to the new reality. There is a new equilibrium
associated with new state variables (treating expectations as a state variable).
The breaking of the bubble does not itself destroy any physical or human
capital, so in principle, with efficient markets, these resources should be fully
used. Standard theory (and real business cycle theory) says that, given any set
of state variables (including expectations of the future, and debts) there exists a
set of wages and prices such that all markets clear—including the labor market.
Given the elimination of the distortion caused by the “bubble” prices, real output
should actually be increased. Sometimes there is a quick recovery (as in the
hoped for V-shaped recovery). But sometimes (as in the Great Depression and
in this recession) the recovery is very slow. The effects of adverse shocks
persist. This section focuses on the third puzzle: Why is recovery so slow.
Slow Price Adjustments. There are two strands of explanations. One, the more
traditional, focuses on wage and price rigidities. Many of the standard
explanations of these rigidities are not fully persuasive: Staggered wage setting
models don't fully explain why it is that those wages which are fully adjustable
don't fully absorb the shock.28
There are more plausible bases of slow wage and price adjustments, based on
firm risk aversion, which itself can be derived, for example from agency theory
or financial constraints and the fact that firms know more about their current
position (wages, prices) and what might happen were they to alter wages and
prices by a small amount than they know about the consequences of large
changes.29
Slow Recovery of Balance Sheets. In the previous section, I explained how
shocks to firm and bank equity can have large macroeconomic consequences;
but rebuilding balance sheets takes time. As we have noted, firms’ ability to
borrow is limited by their equity; because of capital market imperfections raising
equity is very expensive, so that most firms rely on retained earnings. Restoring
the lost equity is thus a slow process.
Market Instability: Economic Intuitions. Even if wages and prices were more
flexible (or conceivably, perfectly flexible) it would not necessarily imply a quick
restoration of the economy to full employment. Especially with information
imperfections and incomplete markets, market adjustments to a perturbation
from equilibrium may be (locally) destabilizing.30 Standard economic theory has
little to say about out-of-equilibrium adjustment. Walrasian tantamount theory is
of little relevance to the real world in which adjustments occur in real time, and
there are real consequences (for example capital losses/gains) that result in
making transactions at the wrong time.
Traditional Keynesian economics emphasized one aspect of the destabilizing
dynamics: cuts in wages in response to unemployment lowered aggregate
demand, thus increasing unemployment. Even the supply-side effect (that lower
wages lead to increased demand for workers at any given level of employment)
may be obviated by adjustments elsewhere in the system. What matters is real
wages, not nominal wages, and lower wages shift the supply curve, increasing
downward pressure on prices. If prices fall in tandem with wages, real wages
will remain little changed. These real wage rigidities are not caused by unions,
but follow from natural assumptions concerning the decentralized adjustments
to disequilibrium in goods and labor markets (Solow and Stiglitz 1968).
Inventory reductions, to “liquefy” balance sheets (described earlier) put further
downward pressure on prices.
Fischerian debt-deflation dynamics gives rise to an even stronger set of
destabilizing effects (Fisher 1933; Greenwald and Stiglitz 1993, 2003a).
Because debt contracts are not typically indexed, what matters is not actual
deflation, but simply inflation that is lower than expected: any such outcome
adversely affects the firm's balance sheet, which, given capital market
imperfections, may actually lower aggregate demand—increasing the gap
between supply and demand. Firms’ demand for investment and their ability to
raise finance for investment are impaired. There are further feedbacks through
the financial system. Higher rates of default weaken bank capital, leading to
less credit availability and higher lending rates (examples of the phenomenon of
trend reinforcement noted earlier, Battiston et al. 2010). There is even the
possibility of a bankruptcy cascade. Lower housing prices may lead to more
foreclosures, putting further downward pressure on prices. In short, lower prices
may lead to further contractionary pressure.
Asset price adjustments too can give rise to an adverse dynamic through other
channels: With a reduction in asset prices, in what might be viewed as a
salutary correction to a bubble, the value of collateral is reduced, but with less
collateral, especially small- and medium-sized firms cannot borrow, either for
working capital or investment, so there are further contractionary effects.
Adverse supply effects (for example from asset price adjustments) reduce the
demand for labor and put downward pressure on wages. As workers’ incomes
fall, so too does their demand for consumption goods, an effect which is not
likely to be fully offset by capitalists’ higher consumption as a result of higher
profits. Again, aggregate demand is reduced.
Other factors discussed earlier as part of the amplification process too come
into play in slowing the process of recovery. For instance, large price changes
give rise to increased uncertainty, for example about balance sheets. Increased
uncertainty impedes firms’ willingness to make investments and banks
willingness to lend. Moreover, because some prices (for example, equities,
commodities) are determined in auction markets, and others (much of
manufacturing) on the basis of posted prices, adjustments to shocks may, in the
short run, lead to relative prices that are badly out of line (Stiglitz 1999a), with
large changes in balance sheets and high levels of uncertainty—often in a
destabilizing manner. In the absence of distributional effects and financial
constraints, such price changes might be of second-order importance. Sellers
lose, buyers gain, but in a representative agent model, since the seller and
buyer are the same person, nothing happens. But more generally, distribution
matters: if prices of agricultural goods fall rapidly, farmers reduce their spending
by more than urban workers and rentiers increase their spending. Aggregate
demand thus falls. More generally, with both supply and demand concave
functions of firm equity, there are real, and potentially large, consequences to
such redistributions.
Expectations, too, can have a short-run destabilizing effect. If, as now, interest
rates are close to zero, were prices expected to fall, real interest rates would
rise, and (at least in the standard theory) the increase in the real interest rate
would decrease aggregate demand, reinforcing the shortfall between supply
and demand. What matters, of course, is the expected real interest rate, which
depends on the expected change in prices. And here again, there can be
(locally) destabilizing effects of price adjustments: a fall in the price today can
lead to expectations of further decreases. Consumers, rather than consuming
more, may postpone purchases, waiting until prices are still lower.
So far, I have focused my discussion on destabilizing movements in prices and
wages. But market-based exchange rate adjustments can also be very
destabilizing. In the East Asia crisis, worries about the future of the economies
led to a decrease in the exchange rate; but these countries had foreign
exchange-denominated liabilities, so a fall in the value of their exchange rate
worsened their economic position, facing many firms (and banks) with the risk
of default; and these adverse effects more than offset the benefits from
increased competitiveness of exports.31
Some of the instabilities that I have discussed are a natural part of
decentralized market adjustment; but some (and others I shall note shortly) are
the result of policies. In the East Asia crisis, the IMF and the US Treasury
demanded that interest rates be raised to high levels to slow the fall in
exchange rates. But the high interest rates pushed large fractions of the firms in
the affected countries into financial difficulties (in the worst cases, into
bankruptcy); the resulting economic disruption exacerbated the economic
decline and worsened the exchange rate, with the follow-on effects described
earlier.
One of the objectives of our models should be to provide insight into the design
of stabilizing policies. For instance, rigid capital adequacy standards for banks
can be destabilizing; countercyclical capital adequacy standards (or what is
sometimes call macro-prudential regulation) can be stabilizing (see for example
Griffith-Jones et al. 2010).
The Fight over Who Bears Losses after a Bubble Breaks—and over Control of
Assets. After a bubble breaks, it is typical that the value of claims on assets
(what debtors owe creditors) exceeds the value of assets. Someone has to bear
the losses; there is a fight over who bears the losses. But this fight—and
resulting ambiguity in long-term ownership—contributes to the slow recovery
and the magnitude of losses. When there are no clear owners of an asset,
assets are not well maintained. Indeed, there is a risk of asset stripping. One of
the costs of home foreclosure is the lack of care (or worse) that occurs in the
process. Recognizing this and the high transactions cost associated with
bankruptcy, one might have thought that there would be ex post renegotiation:
the bank (or owner of the mortgage) will only be able to recoup the current
value of the house, so both the lender and the borrower would seem to be
better off with a write-down. But one of the reasons that the market is in
paralysis is that there are typically two (or more) mortgages. The second
mortgage holder, who in the event of foreclosure will receive nothing, has
nothing to lose by refusing to renegotiate, unless the holder of the first
mortgage agrees to significant concessions. With those responsible for
renegotiation, the service providers, being owned by the holders of the second
mortgage, there is a clear conflict of interest; and anticipating this, some
securitizations limited the scope for renegotiation.
A standard result in the theory of bargaining with asymmetric information is that
the renegotiation may entail large inefficiencies, such as strikes in labor
disputes; both workers and employers are worse off with a strike. Both the
borrower and the lender are worse off with a foreclosure. Still, this is the market
equilibrium (Farrell 1987).
Economic policy can, of course, affect the resolution of the problem of excess
claims. In the case of a systemic crisis, given the large number of separate debt
contracts, restructuring through ordinary bankruptcy processes is difficult, which
is why a Super-Chapter 11, an expedited way of handling such workouts, is
needed (Stiglitz 2000; Miller and Stiglitz 1999). In some cases, it may be
necessary to force restructuring. In the current crisis, there is a need for debtequity swaps for homeowners (banks would get, say, 80% of the capital gain on
the sale of the house, homeowners would pay rent on the basis of the
appraised value of the home).32
To avoid the large distributive consequences associated with open
restructuring, many countries have chosen two alternatives. One is to exploit
the fact that the claims of the creditors are denominated in nominal terms.
Inflation reduces the real value of those claims, enabling the debtors to repay.
This is a debtor-friendly approach, which not surprisingly is typically strongly
opposed by creditors. The other alternative (the one the United States is
choosing) is to muddle through, with non-transparent accounting allowing banks
to postpone recognizing losses, and therefore postpone recapitalization. In the
meanwhile, banks are being recapitalized through a hidden subsidy, the large
spread between the lending rate and the rate at which they have access to
funds. But unfortunately, this inefficient approach to restructuring contributes to
the slow recovery.
Jump to…
4. How Economic Progress Has Made Our
Economy More Vulnerable
In one interpretation of the current crisis, it represents just a large realization of
a negative exogenous (technology) shock. It was just a once-in-a-100-year
flood. Most observers (see Financial Crisis Inquiry Commission 2011) believe,
however, that it was not inevitable, and that the bubble was endogenous, and
could have been avoided—or at least its magnitude and consequences
lessened. One of the objectives of policy is to reduce the frequency and size of
adverse shocks. I believe that changes in our economy—many associated with
government policy—have in fact made our economy more vulnerable. In this
section, I propose four hypotheses which, with further elaborations, provide, I
believe, considerable insights into what has happened, and why the downturn in
the West has been so severe, with such a slow recovery. The four hypotheses
focus, respectively, on risk, information, credit, and structural change. Each lies
outside the boundaries of analysis of the Standard Models.
The picture of the crisis that they provide is different from that of the real
business cycle, where the economy efficiently absorbs and adapts to
exogenous shocks. This crisis, I believe, is more akin to the credit cycles that
have marked capitalism for the last 200 years, but there are some distinctive
aspects of this credit cycle. They are likely to result in this downturn being more
prolonged than a “normal” credit cycle, unless appropriate palliative measures
are taken. Moreover certain policies—based on misunderstandings of the
functioning of the economy—have contributed to creating the crisis; reforms in
these policies are desirable.
4.1. Risk
Our first hypothesis is that there have been large (and often adverse) changes
in the economy's risk properties, in spite of supposed improvements in markets.
Central to macroeconomics should be an analysis of the economy's risk
properties, which includes (i) its exposure to risk, (ii) how it amplifies or
dampens shocks, and (iii) how individuals and firms are affected by risk. A
corollary of the Greenwald–Stiglitz theorem is that privately profitable risk
transactions and innovations may be socially undesirable. There is thus a
potential rationale for government intervention to regulate and manage
economic risk and risk sharing/transferring arrangements.
There are several reasons why, in recent years, in some key respects, the risk
properties of the system may have changed for the worse: Ideas, interests, and
innovations led to the belief that risk could be handled better, so that more risk
could be assumed. But the improvements were less than had been widely
thought, with the result that the system actually became more fragile, and
especially more sensitive to large and correlated shocks. Indeed, this was
perhaps the biggest flaw: there were changes in economic structure that
enhanced its performance in handling small risks, but worsened its performance
in handling large risks (Haldane 2009; Haldane and May 2010; Stiglitz 2010c,
2010d). Regulations were stripped away and regulators who didn't believe in
regulations were appointed, while financial innovations helped markets
circumvent the regulations that remained. Other financial market “innovations”—
floating rate mortgages—shifted risk to those least able to bear it, homeowners;
while still others (securitization) contributed to information asymmetries (moral
hazard), leading to lower-quality mortgages, with more systemic risk.33 The
movement from fixed to floating exchange rate systems too has arguably forced
businesses to bear more risk associated with exchange rate volatility. In these
and other areas, financial markets have not always introduced products that
might have helped individuals better bear the risks that they face. In some
cases they have actually resisted the introduction of such products (inflation- or
GDP-indexed bonds), behavior partly explicable in terms of the difference in
their interests (maximizing transaction costs) and social interests. See Stiglitz
(2010a) for other examples and alternative explanations of such behavior.
Beliefs and interests combined to lead to policies which exacerbated risk. The
repeal of Glass–Steagall led to an enormous increase in banking
concentration—too-big-to-fail banks had an incentive to engage in excessive
risk taking. The ballooning derivatives markets were left unregulated. Especially
in developing countries, capital and financial market liberalization exposed
countries to more risks. Capital market liberalization may increase consumption
volatility and lower expected utility.34
In many countries, automatic stabilizers have been weakened, and automatic
destabilizers strengthened. The change from defined-benefit to definedcontribution pension systems—motivated in part by mistaken beliefs about the
best way to manage risk, without any cognizance of the systemic
consequences—left individuals to bear the brunt of volatility in asset prices, with
greater sensitivity in aggregate expenditures to these asset price changes than
when firms had to bear the risk.35 Widely used managerial compensation
schemes encouraged excessive risk taking and short-sighted behavior, with
macroeconomic consequences.
Repeated bailouts of the banking system also had adverse consequences (see,
for example, Soros 2008). Because those in the advanced industrial countries
had, by and large, been spared, some made the wrong inference—that markets
worked well on their own, contributing to the view that deregulation was
desirable. But they hadn't really worked well on their own. Moreover, the
repeated bailouts gave rise to moral hazard, especially among the too-big-to-fail
banks: they had learned that regardless of how reckless they were in their
lending, they would be rescued by the IMF and G-7, with the taxpayers picking
up the tab.
But perhaps the central problem with the CW was that it paid insufficient
attention to the architecture of risk, to the general equilibrium properties of the
system. The theory was that diversification would lead to lower risk, and a more
stable economy. It didn't happen, raising the question, where did “theory” go
wrong? Part of the answer lies in themes that we have repeatedly stressed:
corporate governance and irrationality. Bank managers had perverse
incentives, which led them to retain much of the risk, in non-transparent ways.
They could thereby record high profits from transactions, reaping large
bonuses. Moreover, they (and others in the financial market, such as the rating
agencies) systemically and irrationally underestimated the risks.
But there is another part of the answer, reflected in the schizophrenic approach
of the conventional approaches to policies toward financial integration, which
emphasized the benefits of integration before a crisis, but afterwards, focused
on the risks of contagion—which were increased by integration. Because the
CW assumed that crises never occurred, in advocating financial integration,
there was never a discussion of the adverse effects that might arise after a
crisis occurred. What was so remarkable about this approach was that it was
pursued in the face of repeated crises—more than a hundred during the past 30
years. Not only did the models fail to explain the increasing frequency of crises
and simultaneously consider the benefits and risks of integration, they made
mathematical assumptions in which spreading risk necessarily increased
expected utility, ruling out risks of contagion.
It should have been obvious, however, that with non-convexities (for example
associated with bankruptcy, R&D) financial integration can lead to lower
economic performance. Optimal electric grids recognize that there are
advantages of “sharing” capacity over a larger area (the total generating
capacity required to achieve a given probability of a brownout or blackout is
reduced), but that a fully integrated grid also increases the risk of system wide
failure. To reduce such systemic risk, circuit breakers are required. Yet, the CW
was adamant in opposition to the analogous concept, capital controls.36
Recent research attempting to reflect both the advantages and disadvantages
of integration has upset the conventional wisdom. More generally, full
integration is not desirable, and under some circumstances (for example, in the
absence of capital controls) no integration may be better than full integration
(Stiglitz 2010c, 2010d). Analogous issues arise concerning the architecture of
risk sharing within a country, say among financial institutions. Risk-sharing
contracts among the banks that were supposed to make the system more
stable had just the opposite effect. Interconnectivity can help absorb small
shocks but exacerbate large shocks, can be beneficial in good times but
detrimental in bad times.
Economic systems can differ not only in the extent of risk sharing, but also in
the pattern. Hub systems may be more vulnerable to systemic risk associated
with certain types of shocks. Many financial systems have such concentrated
nodes. In this perspective, the real problem in contagion is not those countries
suffering from crisis (dealing with that is akin to symptomatic relief) but the hubs
in the advanced industrial country. More generally, poorly designed structures
(including those without circuit breakers) can increase risk of bankruptcy
cascades.37
Earlier, I commented on how the Standard Model failed to take into account the
incentives for excessive risk taking (arising both out of individual and
organizational incentives, including those associated with too-big-to-fail banks).
But systemic risk can arise as well from correlated behavior of many financial
institutions, each of which is not too big to fail. And there are organizational and
individual incentives to engage in such correlated behavior (made so evident by
Charles Prince's remark to the Financial Times in July 2007, about continuing to
dance as long as the music plays—as long as there is still liquidity; Nakamoto
and Wighton 2007). Anyone not going along with the general set of beliefs
would have been punished (Nalebuff and Stiglitz 1983a).
The risk properties of the economic system can be affected by policy
frameworks—and unfortunately, too often, policy makers fail to take this into
account. I have already referred to several examples. Others include: The US
bankruptcy law of 2006 which strengthened creditor rights, provided lenders
with fewer incentives to engage in due diligence in ascertaining creditworthiness. More competitive banking system lowers franchise value, and
accordingly, may lead to excessive risk taking (Hellman, Murdock, and Stiglitz
2000).
4.2. Information
Good information is, of course, central to a well-performing economy. Good
information is also critical for managing risks—taking, for instance, appropriate
actions in a timely way to prevent a crisis. Earlier, I explained how information
imperfections and asymmetries are central to understanding economic
fluctuations; they explain credit and equity rationing, which in turn is key to
understanding the financial accelerator and the persistence of the effects of
shocks; to understanding why banks play a central role in our economy and
why a loss of bank capital (and bank bankruptcy) can have large and persistent
effects.
Our second hypothesis for understanding the current crisis is that changes in
the financial sector (and the economy more generally) have resulted in a
deterioration in the quality of information, with resulting adverse effects on
economic performance. It is obvious that the financial sector failed to perform
well its critical functions of allocating capital and managing risk. The CW
provides little insight into these failures, which, in the structure of their model,
simply couldn't occur. The discussion of this section helps explain these
failures, and what might be done about them.
There are three aspects of this deterioration in the quality of market information,
which we take up in each of the next three sections.
Moving from “Banks” to “Markets” Predictably Led to Deterioration in Quality of
Information. Market advocates praised how securitization enabled the more
efficient dispersal of risk. Interestingly, the main argument in favor of
securitization was never fully persuasive, for there are many ways of sharing
risk besides securitization; for example, the shares of a local bank can be
widely owned. Advocates of securitization never explained why that particular
approach to risk diversification was superior to alternatives. As I shall explain, it
was not.
The most obvious disadvantage of securitization was that, with those originating
mortgages not keeping them, there was a potential moral hazard problem. In
principle, the investment banks who put together the mortgages into securities
were supposed to ensure their quality; further assurance was to be provided by
rating agencies. None of this worked, nor could it, given the numbers of
individual mortgages (with the numbers increasing exponentially in securities
consisting of pieces of securities). Advocates of securitization ignored the
technical difficulties of the task, relying instead on the belief that one could use
statistical information. But such statistical information ignored the differences in
circumstances between mortgages written in the past and the new mortgages:
the mortgages were different, and the incentives for those writing the mortgages
were different. The problems are, in fact, to a large extent inherent with
securitization. Systems that disperse risk inherently weaken accountability and
incentives not just for gathering information, but for ensuring the quality of the
financial products being produced. In well-functioning public securities markets,
information about the securities is, in effect, a public good—all potential buyers
benefit from the information. And it is inherent that, with private provision, there
will be an undersupply. Indeed, this observation is the basis of the Grossman–
Stiglitz (1980) critique of the efficient markets hypothesis: if markets efficiently
transferred information from the informed to the uninformed, there would be no
incentive to gather information.38
Banking is an institutional arrangement for internalizing the benefits of
information acquisition about borrowers. The shadow banking system based on
securitization is not, accordingly, a substitute for the banking system. And the
difficulties for the rating agencies in coming up with a convincing business
model too are inherent: The current one, with those being rated paying, is
rightly criticized for the obvious conflict of interest. But having buyers pay is also
problematic, since in reasonably efficient markets, there will be free-riders not
willing to pay the rating agencies.
Good financial markets not only are supposed to assess who is creditworthy (a
task at which they failed dismally), but they are also supposed to enforce
contracts. Good contract enforcement entails renegotiation. A foreclosure is
expensive, and when the price of the house has decreased, it is generally
Pareto optimal to renegotiate, especially in the context of a non-recourse
mortgage. As we have already noted, securitization also greatly increased
problems associated with the renegotiation of contracts—especially so, given
the conflicts of interest that were built into the system.
Derivatives Market. Developments in derivatives markets provide a second set
of examples on how market “advances” led to poorer information, and
potentially poorer economic performance. A large fraction of this market
consisted of non-transparent, over-the-counter trades, entailing huge
exposures, in the billions of dollars. The previous discussion has emphasized
the risks posed by this kind of interconnectivity, yet market participants were not
in a position to judge even the extent of exposure; and without such information,
there is really no ability for markets to exercise any discipline.
Such non-transparency should not come as a surprise: because markets that
are fully transparent are more competitive, and less profitable, there are strong
market incentives for reducing and impeding transparency. Indeed, some of the
arrangements undermined principles of market decentralization. For example,
with large credit default swaps not cleared through an adequately capitalized
clearing house, knowing the risk of default of any one firm required knowing the
risk position of every firm with which it was financially interlinked—in a vast,
difficult, simultaneous equation system.
Explaining Lack of Transparency. The lack of transparency within the financial
sector was pervasive, and again, for reasons that are easy to understand—and
help explain why the CW models are so far off the mark. Incentive pay, with
bonuses depending in part on stock market performance or certain other
metrics, have been rightly criticized for leading to excessive risk taking and
short-sighted behavior. But they also encouraged non-transparency, moving
losses or risks off balance sheet, exacerbating already present incentives from
within the regulatory system. “Financial innovations” enhanced their ability to do
this.
The basic point of this section is that the supposed improvements in markets
led to lower information content in markets, and not surprisingly to poorer
overall economic performance.
4.3. Credit Markets
The Standard Models typically summarized the financial sector in a money
demand equation, which determined market interest rates, which in turn
affected consumption and investment behavior. There were no equity and no
credit markets—and no explanation for sudden changes in the supply or
demand for either. They were, presumably, institutional details of little
relevance. In fact, of course, this crisis (like many before) is about sudden
changes in the supply of credit; and such changes are not necessarily closely
linked (in this, as in many other crises) to changes in the money supply.
Elsewhere, Greenwald and I (2003a) have explained the deficiencies in the
Standard Model39 and argued that a principle channel through which monetary
policy affects the economy is availability of credit and the terms at which it is
available (spread between T-bill rate and lending rates), which is an
endogenous variable, affected by conventional monetary as well as regulatory
policies. Even if one focused only on interest rates (denying the relevance of
credit availability), one needs a model to explain that spread—for example, why
it may have increased since the crisis began. But the standard approaches not
only failed to provide such a model based on plausible microeconomic
foundations of the banking sector, but also failed to note the profound changes
occurring in the credit systems in some of the advanced industrial countries
(such as the United States).
Our third hypothesis is: Changes in the financial sector affected adversely the
efficiency of the credit system, and made it more vulnerable to a shock such as
that which occurred. These changes will likely slow the restoration of credit.
There were several key changes, all of which were touted as improving the
financial system, but most of which adversely affected its efficiency and stability,
and none of which were adequately reflected in the Standard Models. One of
these we have already discussed: the development of the shadow banking
system and securitization, or as it is sometimes put: banks went out of the
storage business into the moving business. Many of the seeming advances
were really designed to circumvent the remaining regulations intended to
ensure the stability of the financial system. CMA accounts, allowing the rapid
transfer of money into and out of bank accounts, circumvented reserve
requirements on deposits for checking accounts.
Better risk management supposedly justified higher leverage (reflected in Basel
II), but somehow, in these discussions, the Modigliani–Miller theorem got lost.
What really seemed to be going on was a combination of risk-misperception
and risk-shifting to government, especially by the too-big-to-fail institutions. (As
we previously noted, the problem that they posed had worsened markedly since
the repeal of the Glass–Steagall Act, which contributed to in increased
concentration and risk-taking in banking. A working system with concentrated
banking is not impossible, but it requires tight regulation and close supervision.)
Banks became more reliant on wholesale deposits—which could leave the bank
quickly if confidence in the bank eroded—as opposed to “old-fashioned”
deposits that were more sluggish. Those in the financial sector became
confused between ensuring that real resources were used more efficiently, and
ensuring that “money” earned as high a return as possible. Private and social
returns were often markedly different, the former often a consequence of rentseeking (see for example Stiglitz and Weiss 1990). Indeed, competition for
depositors reduced the franchise value of banks, and this too encouraged
excessive risk taking.
Other institutional changes may also have had adverse effects. The shift away
from partnerships for the big investment banks may have exacerbated shorttermism, partially undermining the long-term relationships which have been the
core of sound credit.
The changes in credit markets helps explain why the recovery from this crisis
may be particularly slow: Banks have lost large amounts of capital, and the
process of rebuilding capital can be slow. The lack of transparency in the
financial sector (upon which we commented earlier) may make raising
additional equity more difficult and costly than it otherwise would be. Bank
capital will be restored through profits, the spread between the lending rate and
the borrowing rate. Government policies of keeping the rates banks pay for
funds low and allowing anti-competitive practices to persist may enable bank
equity to be restored faster than it otherwise would have been. Still, the large
spread between deposit and lending rates, while good for restoring bank
balance sheets, is bad for resuscitating the economy.
Moreover, with many banks having gone out of the old-fashioned lending
business, and with the inherent problems in securitization (at least in some
areas) finally being recognized, a full restoration of the credit supply will be
slow.
4.4. Structural Transformation
The last major crisis, the Great Depression, was a period of structural
transformation—a move from agriculture to industry. The Great Recession is
another period of structural transformation, from manufacturing to the service
sector, induced by productivity increases in that sector and changes in
comparative advantage brought on by globalization. Such major structural
transformations are periods of high uncertainty and occur only episodically, so
that rational-expectations models provide little insights in these situations. This
brings us to our fourth hypothesis for why this crisis may last longer than most
downturns: structural transformations may be associated with extended periods
of underutilization of resources.
With an elasticity of demand less than unity, a sector like agriculture with
increasing productivity has declining income. But, with declining incomes and
high capital costs (including individual-specific non-collateralizable investments)
associated with transition out of the sector, it may be difficult for those in these
sectors to move out. But declining incomes of those trapped in the highproductivity/declining income sector have adverse effects on other sectors, not
compensated by offsetting increased spending from those employed elsewhere.
Cyclical and structural problems are intertwined. Downturns (and the associated
loss of capital) make financing structural transformation more difficult. Booms
too can give rise to structural problems, with labor, for instance, “trapped” in the
construction sector that the bubble helped bloat.
The United States did not fully emerge from the Great Depression until the
massive Keynesian stimulus accompanying World War II provided the impetus
finally to move the labor that was trapped in the rural agricultural sector into the
urban manufacturing sector. Before the end of the war, there was much worry
that with the removal of the stimulus, the economy would return to its underconsumption path. Yet the transition was remarkably smooth: the war had
succeeded in pushing through the structural transformation that the economy
on its own found so difficult. And the forced savings during the war meant that
there was pent-up demand at the end of the war.
Say's law, that supply creates its own demand, is not true outside of the fictional
world of neoclassical economics. Today, the question is, what will make up for
the reduced spending resulting from the declining incomes of workers, for
example in manufacturing and construction; or the reduced spending that
results from the adverse shock to household wealth from the breaking of the
bubble, that has left them with their mortgage debt, vastly overleveraged? And
to what sectors will those American men—formerly in manufacturing and
construction jobs—go? Some countries, like Germany, have maintained a hightech manufacturing sector, but that requires a different set of educational and
industrial policies than those traditionally employed in the United States. The
service sector has been sold as the sector of the future, but the high paying
jobs in that sector were in finance—and that sector, it is now recognized, was
overbloated, before the crisis receiving more than 40% of all corporate profits.
The services people want include especially education and health, but these
have been traditionally largely publicly funded, and with current budget
stringencies, prospects of such expansion are weak.40
There are, of course, many areas where there are high social returns to
investment (large capital shortages in the least developed countries, retrofitting
the world for global warming). If funds could be recycled to those uses, there
would be no shortage of global aggregate demand. Prior to the crisis, global
financial markets did not work well: it is hard to believe that the best use of the
world's scarce capital was constructing homes for Americans beyond their
ability to pay. I am not sanguine that global financial markets will succeed in
channeling funds to these areas of high social return—with the resulting risk of
persistent lack of adequate global aggregate demand.
In this section, I have focused on four ways in which the US economy (and that
of many other advanced industrial countries) have been changing that have
contributed to the making of the crisis, and will likely contribute to a slow
recovery: This crisis combines elements of increased risk, reduced quality of
information, a more dysfunctional credit market, and a structural transformation,
with two more ingredients:
(a) Growing inequality domestically, which would normally lead to a lower
savings rate (though not so in a representative agent model where distributional
issues simply don't arise). The adverse effects were obfuscated by growing
indebtedness, itself supported by a bubble based on low interest rates, lax
regulation, and irrational expectations.
(b) The build-up of reserves in emerging markets. One of the factors
contributing to under-consumption (high savings) is growing global reserves—
now in the trillions. The growth increased markedly towards the end of the last
century. One factor was the East Asia crisis and the way it was managed by the
IMF and US Treasury. Emerging-market countries did not want to lose their
economic sovereignty, or to be subjected to policies which forced them into
recessions and depressions. To avoid this, they rapidly increased their
reserves—that is, there were high levels of global precautionary savings.
Moreover, many countries had learned of the benefits of export-led growth, and
low exchange rates (with associated trade surpluses and reserve
accumulations) promoted export-led growth.
Regrettably, the way this crisis has been managed has shown the virtue of
large reserves. And the high level of unemployment in the advanced industrial
countries has continued to impose downward pressure on wages. In short, two
of the underlying factors contributing to the crisis have become worse.
The interpretation of the crisis provided in this section has some direct policy
implications. These include a focus on creating a global reserve system (to
obviate the need for individual countries to build up reserves); strengthening
that part of the financial system which provides credit to small- and mediumsized enterprises, creating a new mortgage system (perhaps along that which
has been so successful in Denmark); and industrial policies aimed at facilitating
the restructuring of the economy. All of this will take time and, meanwhile, there
is need for government expenditures.
Jump to…
5. Policy
I began this paper by noting the failure of the Standard Models to predict the
crisis. But an even more telling criticism of those models is that they provided
policy prescriptions which contributed to the crisis, and provided inadequate
guidance to what should be done in response. The standard view was that
markets could manage risk on their own; low and stable inflation was necessary
and almost sufficient to obtain a low output gap, and perhaps even ensure high
growth. Because of the belief in the efficiency of markets, there was hesitancy
by central banks to use the full arsenal of tools in their toolkit—including
regulations that could have dampened or perhaps even prevented the bubble.41
In spite of protests that one couldn't be sure that there was a bubble until after it
broke, prices were so far out of line with historical norms that policymakers
should have been very worried—and after all, all policy making is done with
uncertainty. The models led policymakers to believe that, even if there were a
bubble, with global diversification, the impact on macroeconomic activity would
be small. It would be cheaper to clean up any mess afterwards, than to interfere
with the wonders of the market.
Admittedly, all of these policy stances look a little ridiculous from our current
vantage point. In this lecture, I have tried to explain some of the central reasons
that the models went so badly astray: for example, lack of attention to credit,
and to the vast array of other problems posed by imperfect information
(including agency issues, corporate governance, etc.). These are not secondorder refinements: they explain why we have banks and what affects banks,
and the limits of securitization. That some central banks employed macromodels in which banking, presumably their raison d’être, was virtually absent is
remarkable.
One of the spurious attractions of the DSGE models is that they provide
quantitative guides to policymakers who have to make quantitative decisions:
for example, about changes in the interest rates. But while they provide
numbers, the question is, what confidence should we have in the numbers that
they provide? Decision making is sequential: central banks are constantly in the
process of reviewing and revising previous decisions, and it is the observation
of the short-run responses to previous decisions more than numbers provided
by models of consumers maximizing utility over an infinite horizon that do, and
should, inform this process. (To be sure, real complications arise in complex
non-linear systems, with long lags.)
Each major downturn is sui generis, and the art of policymaking entails
judgments (never based on rational expectations models, simply because of the
distinctive circumstances) about how the distinctive aspects shape current
behavior. In this crisis, for instance, behavior is affected by the unprecedented
(in modern economic history) legacy of debt, weak banks, underwater
homeowners, a securitization market that had come to be responsible for most
mortgages in paralysis, excess capacity in real estate, and a manufacturing
sector that had lost competitiveness. The distinctive policy setting—near-zero
interest rates, some governments, even in advanced industrial countries, facing
high debt-to-GDP ratios, high returns to public investments in the United States
and some other countries because of a dearth of public investments in the
preceding years, political divisiveness—affected firm and household
expectations, again in important ways that could not be meaningfully formally
modeled with rational expectations, simply because the mélange of
circumstances had never occurred. (Note that the list of important factors
affecting economic behavior includes political decisions. Politics and economics
cannot be separated. I have had to give short shrift to these considerations in
this paper.) Putting aside political economy considerations, there are
interventions, based on the same limited information available to private parties,
which are welfare enhancing. But neither theoretically nor empirically do we
have to rely on rational expectations. We can analyze dynamics with adaptive
expectations, and, fortunately, there are a wealth of concurrent surveys and
instruments that provide information about individuals’actual expectations, that
have predictive power.
In such contexts, policy makers inevitably, and rightly, fall back on the old C + I
+ G + X – M analysis, informed, hopefully, by more sophisticated insights,
including those that identify relevant supply-side effects, accompanied by
quantitative assessments that call attention to salient aspects of the current
situation, including expectations data from surveys. Because any short-run
macroeconomic analysis focuses on the determinants to changes (in the short
run) on aggregate demand and supply, any assessment of a particular
modeling framework should ask, to what extent does it enhance our ability to
predict and interpret these movements and to design welfare improving
(employment increasing, inflation reducing) interventions? It is almost surely not
the case that sudden changes in preferences for leisure or adverse technology
shocks caused this or any other major downturn in output or employment. Large
(too large) changes in asset prices have arguably been more important in
leading to volatility than too small changes in nominal wages (see Easterly et al.
2001a, 2001b). There may, in fact, be important changes in the “wedges”
between marginal rates of substitution and marginal rates of transformation
over the business cycle, but these are not exogenous shocks but endogenously
determined, in economies with imperfect competition in labor and product
markets.
Key to an analysis of aggregate demand and supply is a sensitivity to the
appropriate level of disaggregation, for example among individuals, firms, and
assets. Because individuals and firms differ, redistributions matter.42 Large
redistributions (across individuals, firms) play a role in explaining volatility; and
government policies aimed at redistributing income (towards individuals with
higher marginal propensities to consume) or access to finance (towards firms
which are financially constrained) can lead to increases in aggregate demand.
Not all tax cuts or expenditures increases are created equal: some have larger
multipliers than others. For instance, increased unemployment benefits have a
high multiplier, because those individuals are often finance constrained. Tax
cuts or increased transfers for the poor elderly are also likely to have high
multipliers: they will spend it all; contrary to the infinite lifetime models, bequests
for most individuals are not important. Tax cuts for high-income individuals may
have low multipliers. These are the individuals for whom, if Ricardian
equivalence ever holds, it holds.43 Indeed, for high-income older individuals, an
increase in the inheritance tax may induce more consumption.
Thus, high-return government investments financed by taxes on households
with low marginal propensities to consume or taxes on firms that are not
financially constrained may increase aggregate demand, output, and
employment in the short run. Even when such investments are financed by
deficits, they may reduce the national debt in the long run, because of the
increased tax revenue that they generate. DSGE models remind us, of course,
to look at potential long-run impacts: in these cases, it is the lowering of the
debt (and future tax payments) that reinforces the current impacts in stimulating
the economy. That so many of the models came to the opposite conclusion
simply reflects that conclusions follow from assumptions: if one assumes that all
government spending is for consumption, and constructs models with low shortrun multipliers, then there will be adverse long-run effects on the national debt.
Indeed, if government capital is complementary with private capital, then
government spending can even lead to increased investment for firms that are
not financially constrained.
There are other dynamic effects that may be of importance in the medium term,
to which policymakers should pay attention, but which were given short shrift in
the Standard Models. Firm and bank net worth matter (there are difficulties in
raising new equity because of information imperfections). An increase in
aggregate demand today increases firms’ net worth, and hence aggregate
supply and demand in future periods. An increase in banks’ net worth increases
their ability and willingness to lend. That's why it was such a mistake to allow
them in the midst of the crisis to pay out so much in dividends and bonuses.
(This is a standard result of banking models; but it was not incorporated in the
macro-models). High extended unemployment rates give rise to hysteresis: we
saw it in Europe in the 1980s, we are likely to see it in the United States. If so, it
suggests that there are particularly high social returns to preventing the
unemployment rate from rising to the levels to which it has risen. Labor supply
can also be affected by retirements, and large losses in retirement incomes are
inducing many to postpone retirement. With fewer flows out of the labor force,
the problem of finding jobs for the new entrants becomes worse. Finally, as we
noted in Section 4, the overhang of leverage and of excess capacity in housing
affects the economy well after the bubble has broken.44
While these observations fall out naturally from models which reflect the
presence of financial constraints and firm and individual heterogeneity, they are
more than theoretical truisms: There is ample empirical evidence of the
relevance of the distinctions; unemployed and poor aged have close to unitary
marginal propensities to consume; small firms are more likely to be financially
constrained than large firms. So too, since the effects of monetary policy are
significantly mediated through the banking system, a central objective of policy
analysis should be an understanding of the determinants of banks’ ability and
willingness to lend and the terms at which they make loans available. But just
as not all households are the same, neither are the banks. Some are more
connected to the “real” sector—more engaged, for instance, in lending to smalland medium-size enterprises, where securitization is not an option. Ensuring
their quick recovery may be central to rapid economic recovery. These are the
issues on which models aiming to give us insight into how monetary and fiscal
policy can stabilize the economy, particularly after a deep downturn, should
focus.
The appropriate level of disaggregation is also essential in analyzing how the
particular situation of the moment differs from previous situations, which form
the basis of the estimated empirical relationships upon which we might rely in
more “normal” times. For instance, multipliers today may be markedly higher
than they have on average been in the past. Highly leveraged households—of
which there are many now—may not have access to funds, or have access to
funds at very, very high rates (little connected with the T-bill rate). So too,
estimates of the elasticity of investment with respect to the real interest rate
based on past experience are of limited relevance in the midst of a crisis such
as the current one: The questions now are, What is the interest elasticity when
there is large excess capacity—and where there is expected to be excess
capacity for an extended period of time? How does lowering government
interest rates (short or long) affect the availability of capital, and the terms at
which it is available, especially to SMEs, when the banking system itself is in
trauma?
One of the reasons that the situation today is markedly different (and multipliers
are likely to be markedly larger) is the policy setting: for many countries, this is
the first time that the zero interest rate bound (which was given short shrift in
the literature) is binding—and is binding for an extended period of time. When
the economy is at full employment, one expects monetary authorities to offset
increased government spending, so one expects zero multipliers. But the
economy is far from full employment. One source of leakage is savings; but
savings leads to future consumption. If the economy is at full employment at
that future date, then the increased output at that future date will be offset by
monetary policy. But if the economy is not (expected) to be at full employment
in that future date, then the cumulative multiplier will be larger—and there will
be a feedback (with rational expectations) to the present period.
Moreover, the policy of monetary authorities (critical in determining multipliers)
should not be viewed as exogenous, beyond the government's control (indeed,
a major objective of macroeconomic modeling is to prescribe what monetary
policy should do).45 We should not say that fiscal policy is ineffective simply
because it will be offset by monetary policy; we need to ask, would fiscal policy
accompanied by appropriate monetary policies be effective in stimulating the
economy.
There are other implications of our analysis for policy stances: The zero rate
bound means, in particular, that monetary policy cannot offset spending cuts; so
the usual response to excessively aggressive fiscal consolidation—if
unemployment increases, the monetary authorities can simply lower interest
rates—is no longer applicable.46
In short, even if in the past an increase in government spending on average
was largely offset by reduced private spending as a result of “wealth” effects
(expected future taxes) or policy responses (Fed increases in interest rates), it
does not mean that that will be the case now. The kind of spending being
contemplated today may be different (investment rather than consumption).
Even if in both cases there were investments, the marginal return to public
investment or the level of government indebtedness might have been different.
Financial constraints may affect firms and households in different ways. And the
policy responses (for example, by the Fed) can differ.
Lucas rightly emphasized that behavior might have been different under
different policies, which is why ascertaining the right structure is so important.
Many of the policy discussions under debate today, for instance, focus on
inducing behavioral changes on the part of banks and other financial
institutions, seen to be central to creating a more stable macro-economy; but
remarkably, little attention has been given within macroeconomics to
understanding the behavior of these institutions, including the role of agency
problems, accounting standards, and their behavior towards risk. Expectations
are one ingredient in determining responses to policy changes. But so too are
the structural features, including the differences between firms, households,
and financial institutions upon which we have focused.
Jump to…
6. Concluding Remarks
I have provided here a multi-faceted critique of the reigning paradigm in
macroeconomics. First, I have raised several methodological critiques. (a) The
Standard Models focused on the wrong questions. They focused on explaining
the small “normal” variations in the economy—which don't matter much—and
ignored the large variations which matter a great deal. They asked how the
economy responded to exogenous shocks, while some of the most important
disturbances—the bubbles that periodically occur, and then break—are clearly
endogenous. They should have focused on how and why market economies
amplify shocks, why the effects persist, and why the economy does not quickly
recover.
(b) The approach to model verification was suspect. The focused on how well
the model did in the periods in which things worked well; not how poorly the
model did when we really cared about the model predictions. Indeed, some
currently fashionable strands do little more than curve fitting in an underidentified system, an approach that is a step back from the advances that had
occurred in previous decades in statistical inference. (Korinek (2010d) puts the
criticism well: “First, the set of moments chosen to evaluate the model is largely
arbitrary… Second, for a given set of moments, there is no well-defined statistic
to measure the goodness of fit of a DSGE model or to establish what
constitutes an improvement in such a framework.”)47 Moreover, they did not look
at all the predictions of the theory, only a selected subset, ignoring the fact that
some of the testable predictions could be rejected.48
Secondly, the Standard Models make special assumptions—about behavior
and about economic and mathematical structures—which limit their generality
and relevance. I have noted several that have played a key role in the results.
Here, I want to note one more: The mathematical assumptions which
necessarily implied that systemic risk was reduced through diversification is an
example of special (and sometimes not obvious) assumptions which bias the
result of the analyses. The schizophrenic approach that assumes convexity ex
ante—so that risk can be fully diversified throughout the system—and when it
evidently has not been, worrying about contagion, is unsatisfactory. We need
coherent models that incorporate key nonconvexities.
I have emphasized trade-offs in modeling. The standard approach was too
ambitious in some respects, not ambitious enough in others; in the end, it made
the wrong trade-offs. Complexities arising from intertemporal maximization over
an infinite horizon are far less important than those associated with a more
accurate depiction of financial markets and other aspects of household and firm
behavior. Future modeling providing greater realism in modeling
banking/shadow banking, key distributional issues (life cycle), key financial
market constraints may necessitate simplifications in other, less important
directions. Many of the limitations on the Standard Model that I have stressed
have been recognized by researchers within the field, and as I have noted,
there have been significant advances in addressing them. But as the discussion
on policy highlighted, I am not convinced of the ability of these models, in their
current state, to address key policy issues.49 The attempt to construct dynamic
stochastic general equilibrium models is admirable, and perhaps eventually, the
profession may achieve the goal of constructing such a model that provides
insights into policy issues that really matter—such as how to prevent bubbles
and the major economic downturns to which they give rise and how to ensure
quick recoveries when they occur. But meanwhile, what is likely to yield the
highest returns are better modeling of the critical sectors and behavioral
components. In this paper, I have focused particularly on the financial sector.
The art and science of macro policymaking will involve blending the insights
from these partial models into a consistent macroeconomic framework.
Fortunately, over the past 30 years, we have made enormous progress on this
alternative agenda. The task is to put the various pieces together in a tractable
manner, to formulate a New Macroeconomics. The New Macroeconomics will
need to incorporate an analysis of risk, information, and institutions set in a
context of inequality, globalization, and structural transformation, with greater
sensitivity to assumptions (including mathematical assumptions) that effectively
assume what was to be proved (for example, with respect to benefits of risk
diversification, effects of redistributions). Agency problems and macroeconomic
externalities will be central. It will have to be predicated on an understanding
that in the presence of imperfect information and incomplete risk markets,
market economies are not necessarily either efficient or stable. These problems
are not just minor foibles, to be glossed over in the praise of the virtues of
capitalism. New policy frameworks need to be developed based on this new
macroeconomic modeling. For a start, there will be a focus not just on price
stability but also on financial stability. Even such a small change will be a good
beginning: the stakes are great, the costs of those who paid excessive attention
to economists’ flawed models have been enormous.
Footnotes
1 Of course, Smith himself took a broader perspective on self-interest
than his modern-day disciples, one which recognized some sensitivity to
the effects of one's actions on others. See, for instance, Nick Phillipson
(2010). Indeed, Rothschild (2001) and Kennedy (2009) argue that Smith
used the term “invisible hand” with some irony—with markedly different
views about market perfection than those held by Smith's latter-day
descendants.
2 On 28 March 2007 in testimony before the US Congress, after the
bubble had already broken, the Fed Chairman asserted: “the impact on
the broader economy and financial markets of the problems in the
subprime market seems likely to be contained.”
3 There is a long list of flaws in the incentive structures, discussed and
documented well before the crisis. See, for example, Stiglitz (1982a,
1987a, 2003, 2010a) and Nalebuff and Stiglitz (1983a, 1983b). These
include: (i) incentive structures should have been based on relative
performance—not, for example, stock market value which could increase
due to an industry shock or to an increase in equity prices; (ii) incentive
structures should have attempted to differentiate between increases in
profitability due to increases in α (hard to achieve) and to β (anyone can
get higher average returns, simply by taking more risk). As designed, the
incentive structures encouraged excessive risk taking and bad
accounting. The compensation schemes were also not tax efficient. In
practice, there was simply a weak relationship between pay and
performance.
4 For a textbook treatment of both the basic classical and new
Keynesian DSGE model, see Galí 2008. For a detailed analysis of the
use of the New Keynesian model to evaluate monetary policy, see
Woodford (2003), and Clarida et al. (1999), and Galí and Gertler (2007).
5 There is a large and burgeoning literature on macro-political economy.
See, for example, Besley (2004) and Besley and Persson (2009). Many
of the reduced-form econometric estimates, for example of what happens
if the government engages in expansionary fiscal policy, are predicated
on predictable policy responses elsewhere in the system, for instance,
from monetary authorities. For a critique, see Section 5.
6 As Korinek (2010c) points out, “If DSGE models abstract from certain
features of reality or, even more, if they need to employ fundamental
parameter values that are at odds with empirical estimates at the micro
level in order to replicate certain aggregate summary statistics of the
economy, then the model is not actually capturing the true
microeconomic incentives faced by economic agents, but is ‘bent’ to fit
the data, as was the case with 1970s-style macroeconomic models.”
7 The problem has been recognized by some of those in the DSGE
tradition. The high elasticity of labor motivated alternative models in
which the extensive margin of employment is more central, for example
Hansen (1985). Defenders of the Standard Paradigm might rightly point
out that any piece of the model (here, that describing the labor market)
could be replaced with a more reasonable specification. One might, for
instance, incorporate search or efficiency wage considerations in the
labor market. As I point out in what follows, no one can object to models
that appropriately model the general equilibrium properties of dynamic
economies facing endogenous and exogenous shocks, so at one level
DSGE is unobjectionable. Part of the concern is with the particular
simplifying assumptions used to make the models tractable. As Korinek
(2010c) points out, tractability often biases results towards very special
specifications in which markets are stable and efficient; in many cases,
key questions of interest are, in effect, answered by assumption. As he
also points out, tractability leads to a focus on the ergodic steady state
(which, given the model structure, is usually unique). Many real-world
processes are not ergodic—and under quite plausible specifications the
equilibrium is not unique.
8 See Stiglitz 1973. For a discussion of other tax paradoxes—and other
aspects of firm behavior that are hard to reconcile with the Standard
Models, see Stiglitz 1982a. Since then, there have been innumerable
attempts to explain the paradox, none of which I find convincing. Most
telling, as the appreciation of the point has grown, a smaller fraction of
funds distributed from the corporate sector to the household sector have
been in the form of dividends. The market seems to have “learned”. But
the process has been slow, and the learning incomplete. Details of the
tax code matter: the earlier observation that with debt finance, the
effective marginal cost of capital is unchanged is true if the tax laws
provided for Samuelsonian “true economic depreciation”. Since virtually
all tax systems have depreciation allowances which are accelerated
relative to true economic depreciation, at the margin, debt-financed
investment is effectively encouraged.
9 See Committee on Oversight and Government Reform 2008.
Greenspan also said: “I made a mistake in presuming that the selfinterest of organizations, specifically banks and others, were such … that
they were best capable of protecting their own shareholders and their
equity in the firms.”
10 Besides the dividend paradox noted above, there are many other
instances of market irrationality (see e.g. Shiller 2000; Stiglitz 1982a,
1982b). Economic theory during the last 30 years has “explained” the
persistence of many of these seeming anomalies, For instance, takeover mechanisms (see, for instance, Stiglitz 1972a, 1982b, 1985a;
Grossman and Hart 1980; Edlin and Stiglitz 1995) and evolutionary
processes (see Stiglitz 1975, 2010a; Nelson and Winter 2002) often don't
work—at least in the naïve way that market advocates claim, and in the
relevant time frame. Years ealier, Berle and Means (1932) had called
attention to problems of corporate governance that arise with a
separation of ownership and control.
11 Stiglitz (1969) explains how bankruptcy costs modify the standard
Modigliani–Miller theorem that leverage has no costs or benefits. Nor can
taxation explain the drive for leverage: a close examination of America's
overall tax system, taking into account corporate and individual taxation,
including preferential treatment given to capital gains, suggests that if
individuals were rational, the tax benefits are likely small in comparison
to the costs of bankruptcy (ignoring for the moment the benefits
associated with bailouts). See Stiglitz 1973. Signaling and screening
models provide further explanations for limiting leverage.
12 That this is so retrospectively is obvious; but Shiller's work (2008)
makes clear that it should have been obvious ex ante. The models used
for forecasting default rates on securities irrationally ignored correlations
and the chance of price declines—again, something that is clear
retrospectively, but was argued on the basis of theory and historical
experience well before the crisis (Stiglitz 1992c). Greenspan's argument
in favor of variable rate mortgages (see Chapter 5 of Stiglitz 2010a)
suggests a deep lack of understanding of risk sharing in the market.
13 See, for instance, Shiller 2000. Grossman and Stiglitz (1980) explain
why markets cannot be informationally efficient—a view that, in the
aftermath of the crisis, has come to be widely accepted. Much of the
observed behavior can only be explained on the hypothesis of
differences in beliefs. See Stiglitz (1972a), Allen et al. (1993), and
Scheinkman (forthcoming), and references therein.
14 The failure of the rating agencies is related to this quandary: it was
argued that newly invented products had fundamentally changed
markets. If that were true, there would be no basis for relying on past
data to predict future performance. Yet, irrationally, they did. They should
have seen that the new products were worse than the old.
15 The combined implications of rational expectations with common
knowledge and rational behavior are even more peculiar: under standard
assumptions, there would be no trade on the stock market. See Milgrom
and Stokey (1982) and Stiglitz (1982b).
16 When there is a unique equilibrium, one could fantasize that
somehow they all figured out the equilibrium instantaneously. When
there are multiple equilibria (as there typically are), it is hard to envision
how they know which equilibrium to coordinate on. Even when there is a
single rational expectations equilibrium, there is little reason to believe
that the market, on its own, would converge to the rational expectations
equilibrium, at least in the time frame that is relevant for short-run
macroeconomic analysis (see e.g. Bray 1978, 1981). More recent
research has identified conditions under which such convergence holds
using standard learning models (see e.g. Evans and Honkapohja 2001).
Marcet and Sargent (1988) note that when there is an adaptive game
between a government and private sector, where each uses a leastsquare model, the Nash feedback equilibrium to which the economy
converges is inefficient. But as those authors note, Bray and Kreps
(1987) show elsewhere that “least squares learning schemes are
irrational …[because] for example, they embody a Bayesian prior that is
inconsistent with the law of motion” (Marcet and Sargent 1988, p. 171).
There is a similar critique of the hypothesis of intertemporal rationality on
the part of individuals. Individuals learn, from repeated experiments,
about their preferences. Rationality, as used be economists, refers to the
consistency of their choices, that is, where they arise from the
maximization of a well-defined set of preferences (satisfying certain
restrictions) subject to a budget constraint. But individuals do not have
the opportunity to make intertemporal choices in an analogous manner.
When they come to the end of their lives, they may regret having saved
too much or too little, but there is little they can do with such learning.
There is no way that we can test, at an individual level, the consistency
of such lifetime choices.
17 This is a point of agreement among both the advocates of the New
Keynesian DSGE models (Woodford 2009) and its RBC critics (Chari et
al. 2009). Early versions of NK DSGE models did not even model
unemployment. As Blanchard and Galí (2010) pointed out: “Standard
versions of NK paradigm do not generate movements in unemployment,
only voluntary movements in hours of work or
employment…Paradoxically, this was viewed as one of the main
weaknesses of the RBC mode, but was then exported to the NK model.”
18 There is a large literature, dating back to the Phelps-Winter work on
customer markets (1970) and encompassing labor turnover models
where firms bear some turn-over costs (Phelps 1970; Stiglitz 1972b). For
more recent work, see for example Greenwald and Stiglitz (1995,
2003b). Some New Keynesian DSGE models generate endogenous
cyclical changes to mark ups through specifying particular preferences
(Ravn et al. 2006).
19 The credit availability doctrine played an important role in discussions
of monetary policy within the Bank of England, and the credit channel
episodically received attention in discussions in the United States (see
e.g. Blinder and Stiglitz 1983). For an early survey of the credit channel
of monetary policy see Bernanke and Gertler (1995). For a more relevant
analysis of financial intermediate and macroeconomics, see Woodford
(2010).
20 Sometimes, a distinction is made between the value that could be
achieved if the assets were held to maturity, and the much lower value
achieved if the projects are liquidated prematurely (because of liquidity
demands). But if everyone were convinced of the long-term value, almost
surely there would be someone with resources that would reap the
capital gain of the difference between liquidation and long-term value.
21 Before the crisis, advocates of standard New Keynesian DSGE
models confidently advised monetary authorities not to worry about asset
prices. See Bernanke and Gertler (2001). While the shock was largely
endogenous, exogenous shocks may play some role; for instance, high
food and energy prices (due to a variety of causes, but including weather
and war “shocks”) may have contributed to the timing of the breaking of
the bubble. The real business cycles are, themselves, misnomers, for
there is no pattern of booms and busts, just a series of idiosyncratic
shocks to which the economy responds efficiently. It is perhaps
understandable why this new business cycle literature arose in
opposition to the older literature, the multiplier accelerator models which
gave rise to fluctuations of fixed periodicities. With rational expectations,
both private agents and public authorities would undertake countervailing
actions. Knowing that there would be excess capacity next period, firms
would contract spending this period; and governments should undertake
expansionary policies in a timely way to offset the expected contraction.
22 This is typically the case when there are non-convexities; and nonconvexities are pervasive, whenever there are problems of information
imperfections, R&D, learning, externalities, or bankruptcy costs. See, for
instance, the discussion in Stiglitz (2002, 2010d) and references therein.
The notion of mixed strategy equilibrium in the presence of nonconvexities has been widely discussed. See for example Stiglitz 1975,
1985b, 1987b; Salop and Stiglitz 1977, 1982; Dasgupta and Maskin
1986a, 1986b; Mortenson 2010.
23 Some of these arise with difference equations that give rise to chaotic
behavior. For applications to macroeconomics, see for example
Christiano and Harrison (1999). For an application of agent-based
approaches using the Greenwald–Stiglitz financial accelerator model,
see Gallegati and Stiglitz (1992). But one doesn't have to go to such
complex models to generate patterns of oscillations even with rational
expectations. Stiglitz (2008a) shows that there an infinite set of paths
consistent with rational expectations in a life cycle model, most of which
do not converge to a steady state.
24 This discussion does not fully explain the sudden abrupt change: with
individual heterogeneity, there can (or should) be some smoothing.
Models of amplification discussed later explain the forces that countervail
such smoothing.
25 See Greenwald and Stiglitz 1993; Bernanke and Gertler 1990;
Bernanke, Gertler, and Gilchrist 1999. The microfoundations of the
financial constraints differ in different models, with somewhat different
empirical implications. I believe that those based on adverse selection
(signaling), for example Maljuf and Myers (1984) or Greenwald, Stiglitz,
and Weiss (1984), are more plausible than those derived from costly
state verification (Townsend 1979). Equity constraints combined with
bankruptcy costs lead to risk-averse firm behavior, which may differ
markedly from the risk-neutral behavior typically assumed.
26 See Miller and Stiglitz 1999, forthcoming; Kiyotaki and Moore 1997,
2002; Korinek 2010a. Similar arguments arise in the context of
international exchange rates. These were extensively discussed in the
context of the East Asia crisis (see for example Furman and Stiglitz
1998; Bhattacharya and Stiglitz 2000; Stiglitz 1999b, 1999c), with more
recent theoretical contributions from Korinek (2010b, 2010c) and Jeanne
and Korinek (2010).
27 This again was evident in the crisis (and in the East Asia crisis before
it). Not even well-informed banks knew for sure the balance sheet
positions of other banks with which they interacted, realizing that the fall
in housing prices and the associated price of mortgage-backed securities
meant that there had been large changes in net worth and the risk of
default. In both cases, lack of transparency contributed to these
problems. American banks had, to deceive both investors and regulators,
engaged in off-balance sheet transactions. But they were so successful
that they may have even deceived themselves. The difference between
Lehman Brothers’ supposed balance sheet before and after filing for
bankruptcy bears partial testimony to the magnitude of the uncertainty.
The bankruptcy costs—tallying now more than a billion dollars—shows
why these should not be ignored. It should be noted that with rational
expectations, more transparency can, however, contribute to greater
volatility, as the market responds more to changes in circumstances. See
Furman and Stiglitz 1998.
28 Elaborations on these models can easily do this, but these
elaborations change the model and its policy implications in fundamental
ways. For instance, efficiency wage effects may arise from inordinate
disparities between wages paid to those hired at time t and those hired at
t+ 1. Moreover, one has to have a compelling reason for why such
staggered wage setting persists, when presumably coordinated wage
setting would represent such a large improvement to economic welfare.
29 Greenwald and Stiglitz (1989, 1990). The name menu cost theory
appropriately trivializes the notion that it is the cost of adjusting prices
that is the source of the problem, since the costs of price adjustments are
of an order of magnitude smaller than the costs of adjustment of
quantities; with a shift in, say, a demand curve, either prices or quantities
have to adjust, and given the relative costs, the adjustment should be in
prices. Models where sluggish responses are based on “rational
inattention” (Sims 2003; Mankiw and Reis 2002, 2010) are more
persuasive; but especially large businesses are in fact constantly
monitoring both macroeconomic and sectoral conditions.
30 I do not have space to discuss the underlying mathematics. There
has even been a shift in the past 40 years in what is meant by a stable
system. The representative agent model is viewed as stable, since the
individual, with rational expectations extending infinitely far into the future
converges to the long run equilibrium along a saddle-point. But there is a
sense in which this is a very fragile equilibrium. If the individual
misestimates, he can move along a path satisfying all the short run
equilibrium conditions for a very long time, before he realizes that he is
not on that single saddle-point trajectory. He then will have to make a
large correction. Indeed, Hahn (1966), looking at essentially the same set
of equations describing the economy's dynamics, concluded that the
system was unstable (see also Shell and Stiglitz 1967). In standard
dynamics, a system exhibits long-run stability if, for all initial conditions
(within a range), the system converges smoothly to the steady state. The
“trick” here is the assumption that the market miraculously sets the prices
of all assets instantaneously so that the economy is on the saddle-point
trajectory. Richer models, with heterogeneity and constraints, can give
rise to systems which do not display smooth convergence (see e.g.
Christiano and Harrison 1999).
31 Interestingly, the IMF, normally a believer in market processes,
justified its intervention in the market on the grounds that markets’
adjustments in exchange rates were, in fact, destabilizing, and could give
rise to contagion. Their interventions were designed to stabilize
exchange rates, to slow down adjustment, to prevent “overheating.”
32 Thus, there would be little concern that speculators would take
advantage of such a scheme. With restructuring, banks would, of course,
be forced to recognize losses; as it is, current management would prefer
greater losses in the future rather than recognizing smaller losses today.
Capital adequacy requirements combined with lax rules on writing down
mortgages provide an incentive not to restructure.
33 Some countries have actually banned such mortgages. Some
commentators think that Greenspan's advocacy of such mortgages was
not just an instance of poor analysis but a deliberate attempt to transfer
large amounts of wealth to the financial sector, given his knowledge that
interest rates would be increasing (Taibbi 2010). Other “advances” in
financial markets may have contributed to instability (Caccioli et al.
2009), evidenced most recently by the flash crash of 6 May, 2010,
associated with flash trading.
34 These results have been shown both theoretically and empirically. In
a lifecycle model, for instance, without capital market liberalization, a
productivity shock at time t leads to higher wages not just at time t, but
also, as a result of increased investment at home, higher wages in
subsequent periods. With liberalization, the benefits of the productivity
shock are not shared across generations (see Stiglitz 2008b). For a more
general discussion of the adverse effects of capital market liberalization
see Ocampo and Stiglitz (2008) and Stiglitz et al. (2006). In ongoing
research with Hamid Rashid, we have shown that financial market
liberalization similarly may expose countries to more volatility, as a shock
in the home country gets transmitted through the banking system to the
host country.
35 There can also be procyclical labor supply responses, as in the
current crisis: older individuals, with diminished retirement accounts,
have postponed retirement, or re-entered the labor force. The list of risk
increasing policy changes is long; for example, rigid enforcement of
capital adequacy standards can act as an automatic destabilizer.
36 There are many other instances where CW adherents took a
schizophrenic approach. A standard prescription for handling bank
restructuring was to separate out good loans from bad (unmixing), which
makes sense only because of non-convexities.
37 Among the contributions to this growing literature, besides those cited
earlier, are Greenwald and Stiglitz 2003a; Allen and Gale 2001; Battiston
et al. 2007, 2010; Delli Gatti et al. 2006, 2009; De Masi et al.
(forthcoming); Gai and Kapadia 2010a, 2010b; Gallegati et al. 2008;
Haldane 2009; Haldane and May 2010 and the references cited in these
papers.
38 Anand, Kirman, and Marsili (2010) construct a model in which
whether it pays any individual to gather information and process
information, say about the quality of mortgages, depends on whether
others do. They show that there may exist an inefficient equilibrium in
which no one gathers information. While such behavior may be
individually rational, it is “catastrophic at the aggregate level.”
39 Each of the underlying hypotheses, for instance, of the standard
transaction demand for money are suspect: Money is not needed for
transactions: credit is all that is required; the difference between the
interest rate paid on CMA accounts and T-bills is determined not by
monetary policy but simply by technology and competition; most
transactions are, in fact, not related to income-generating activities but
rather to transfers of assets. For more recent literature attempting to
explain credit spreads, see Curdia and Woodford (2010). Other papers
modeling the credit market include Greenwald and Stiglitz (1993),
Kiyotaki and Moore (1997), and Gertler and Kiyotaki (2009).
40 Besides, the United States is now worried about excessive spending
in health care, though it is one of the few sectors in which there has been
increased employment. Employment in the US health care sector was up
800,000 in December 2010 compared to December 2007 (Bureau of
Labor Statistics Current Population Survey).
41 Like many other tenets, there was some intellectual incoherence: after
all, for the government to set the interest rate is a massive market
intervention. There is no theory that says that government should
intervene in only one place—quite the contrary. (Ramsey's great
contribution was to refute such a belief in the realm of taxation.)
42 There is a large literature within the DSGE framework, surveyed in
Heathcote et al. (2009), modeling individual heterogeneity, but largely
arising from idiosyncratic income shocks. This literature, while claiming to
replicate patterns of income and wealth distribution, ignores the large
microeconomic literature in the field (for example, associated with names
like Atkinson, Shorrocks, Flemming, Aitchison and Brown,
Champernowne), which over a half century has attempted to explain the
Pareto tail, the (near) lognormal form, the role of bequests and imperfect
annuity markets, patterns of observed savings behavior over individuals'
lives, and changes over time and differences across countries, and the
consistency of these patterns with patterns of wealth inequality. More
relevant for macro-analysis, there is no discussion of differing marginal
propensities to consume, which can serve as the basis of stimulative
redistributive policies. Other recent literature attempting to incorporate
non-Ricardian effects includes Coenen and Staub (2005) and Galí,
López-Salido, and Vallés (2007).
43 In the area of fiscal policy, Ricardian equivalence theories illustrate
how direct effects of government deficit spending might be undermined,
as individuals set aside money to pay future tax liabilities. But these
concerns seem of limited relevance in the years before the crisis. As
Bush's tax cuts created massive new deficits, savings rates fell to record
low levels. Do the proponents of Ricardian equivalence really believe
that in the absence of the tax cut, household savings would have been,
say, substantially negative? In more relevant models, incorporating life
cycle effects and financial constraints, Ricardian equivalence does not
hold.
44 Another example is that cutbacks in spending on education,
technology, and infrastructure in response to the increased national debt
will lead to lower productivity in the future.
45 With independent central banks captured by financial markets, one
should perhaps better model their behavior as reflecting the interests of
the financial markets; but a full articulation of this model goes beyond the
scope of this paper.
46 Remarkably, some economists have even warned about government
spending crowding out investment, as more government borrowing
drives up interest rates. But with interest rates near the zero lower
bound, it is hard to see how this could have happened. If anything,
increases in aggregate demand may have reduced deflation/increased
inflation, lowering the real interest rate, crowding in investment (if one
believes that real interest rates drive investment).
47 He concludes by asking: “Should we have greater confidence in
DSGE models that match more moments and that achieve a closer
match to the data than other models? Are these likely to provide a more
useful guide to reality? There is no scientific basis to answer this
question affirmatively. In some instances, the criterion of matching
moments may even be a dangerous guide for how useful a model is for
the real world. The focus on matching moments creates incentives for
researchers (i) to introduce elements that bear little resemblance to
reality for the sake of achieving a better fit (ii) to introduce opaque
elements that provide the researcher with free (or almost free)
parameters and (iii) to introduce elements that improve the fit for the
reported moments but deteriorate the fit along other dimensions.”
48 Guzmán (2009, 2010a, 2010b) provides indirect tests of the rational
expectations in the context of a macroeconomic model, which strongly
reject that hypothesis. She shows (a) forecasts of key economic
variables of “experts” can be improved upon by using concurrently
available data; and (b) forecasts of different groups (men and women)
who presumably have access to the same data (and therefore, with
rational expectations, should have the same forecasts) differ. It is also
the case that (say, the bluechip) forecasts themselves have predictive
power: they can improve upon forecasts that just rely upon past data.
49 This is a view that is shared by Greg Mankiw (2005), who, like me,
served as Chair of the Council of Economic Advisers, which has the
responsibility, under the Employment Act of 1946, for guiding the
Administration in its macroeconomic policies. He wrote that these models
have “had little impact on practical macroeconomists who are charged
with the messy task of conducting actual monetary and fiscal policy.” My
concern, though, is somewhat different: that in mindset, they have had
too much influence. Some of the limitations of DSGE models arise from
the fact that, while recognizing the limitations of current models,
modelers have (understandably) looked for tractable fixes, and those that
may help explain normal fluctuations, but not the deep downturns that
are the focus of my concern. Thus, as I noted earlier, labor market
frictions associated with search (for example Thomas 2008; Merz 1995)
do not explain cyclical unemployment rates of 10%. Nor do staggered
wage and price adjustments and rational inattention models adequately
explain the failure of wages to adjust in a deep downturn. For this,
theories based on efficiency wages, inside-outsiders (Lindbeck and
Snower), and option value (Greenwald and Stiglitz 1995) are required.
Jump to…
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